From 6ac1190315295229859148e1a138e5ccf26870a8 Mon Sep 17 00:00:00 2001 From: Chen Shen Date: Wed, 14 Dec 2022 22:17:55 +0900 Subject: [PATCH 01/34] Bundle for abdominal multi-organ segmentation --- multi_organ_segmentation/LICENSE | 0 .../configs/dataset_0.json | 1688 +++++++++++++++++ .../configs/evaluate.yaml | 64 + .../configs/inference.yaml | 116 ++ multi_organ_segmentation/configs/logging.conf | 21 + .../configs/metadata.json | 99 + .../configs/multi_gpu_train.yaml | 52 + multi_organ_segmentation/configs/search.yaml | 275 +++ multi_organ_segmentation/configs/train.yaml | 354 ++++ multi_organ_segmentation/docs/README.md | 137 ++ multi_organ_segmentation/docs/license.txt | 0 multi_organ_segmentation/large_files.yml | 13 + 12 files changed, 2819 insertions(+) create mode 100755 multi_organ_segmentation/LICENSE create mode 100755 multi_organ_segmentation/configs/dataset_0.json create mode 100755 multi_organ_segmentation/configs/evaluate.yaml create mode 100755 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Activationsd + keys: pred + softmax: true + - _target_: Invertd + keys: + - pred + - label + transform: "@validate#preprocessing" + orig_keys: image + meta_key_postfix: meta_dict + nearest_interp: + - false + - true + to_tensor: true + - _target_: AsDiscreted + keys: + - pred + - label + argmax: + - true + - false + to_onehot: 8 + - _target_: CopyItemsd + keys: "pred" + times: 1 + names: "pred_save" + - _target_: AsDiscreted + keys: + - pred_save + argmax: + - true + - _target_: SaveImaged + keys: pred_save + meta_keys: pred_meta_dict + output_dir: "@output_dir" + resample: false + squeeze_end_dims: true +validate#dataset: + _target_: Dataset + data: "@val_datalist" + transform: "@validate#preprocessing" +validate#handlers: +- _target_: CheckpointLoader + load_path: "$@ckpt_dir + '/model.pt'" + load_dict: + model: "@network" +- _target_: StatsHandler + iteration_log: false +- _target_: MetricsSaver + save_dir: "@output_dir" + metrics: + - val_mean_dice + - val_acc + metric_details: + - val_mean_dice + batch_transform: "$monai.handlers.from_engine(['image_meta_dict'])" + summary_ops: "*" +evaluating: +- "$setattr(torch.backends.cudnn, 'benchmark', True)" +- "$@validate#evaluator.run()" diff --git a/multi_organ_segmentation/configs/inference.yaml b/multi_organ_segmentation/configs/inference.yaml new file mode 100755 index 00000000..aadfe094 --- /dev/null +++ b/multi_organ_segmentation/configs/inference.yaml @@ -0,0 +1,116 @@ +--- +imports: +- "$import glob" +- "$import os" +input_channels: 1 +output_classes: 8 +arch_ckpt_path: "$@bundle_root + '/models/search_code_18590.pt'" +arch_ckpt: "$torch.load(@arch_ckpt_path, map_location=torch.device('cuda'))" +bundle_root: "/work/" +output_dir: "$@bundle_root + '/eval'" +dataset_dir: "/data/" +data_list_file_path: "$@bundle_root + '/configs/dataset_0.json'" +datalist: "$monai.data.load_decathlon_datalist(@data_list_file_path, data_list_key='testing', + base_dir=@dataset_dir)" +device: "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')" +dints_space: + _target_: monai.networks.nets.TopologyInstance + channel_mul: 1 + num_blocks: 12 + num_depths: 4 + use_downsample: true + arch_code: + - "$@arch_ckpt['arch_code_a']" + - "$@arch_ckpt['arch_code_c']" + device: "$torch.device('cuda')" +network_def: + _target_: monai.networks.nets.DiNTS + dints_space: "@dints_space" + in_channels: "@input_channels" + num_classes: "@output_classes" + use_downsample: true + node_a: "$torch.from_numpy(@arch_ckpt['node_a'])" +network: "$@network_def.to(@device)" +preprocessing: + _target_: Compose + transforms: + - _target_: LoadImaged + keys: image + - _target_: EnsureChannelFirstd + keys: image + - _target_: Orientationd + keys: image + axcodes: RAS + - _target_: Spacingd + keys: image + pixdim: + - 1 + - 1 + - 1 + mode: bilinear + - _target_: ScaleIntensityRanged + keys: image + a_min: -500 + a_max: 500 + b_min: 0 + b_max: 1 + clip: true + - _target_: EnsureTyped + keys: image +dataset: + _target_: Dataset + data: "@datalist" + transform: "@preprocessing" +dataloader: + _target_: DataLoader + dataset: "@dataset" + batch_size: 1 + shuffle: false + num_workers: 4 +inferer: + _target_: SlidingWindowInferer + roi_size: + - 96 + - 96 + - 96 + sw_batch_size: 4 + overlap: 0.625 +postprocessing: + _target_: Compose + transforms: + - _target_: Activationsd + keys: pred + softmax: true + - _target_: Invertd + keys: pred + transform: "@preprocessing" + orig_keys: image + meta_key_postfix: meta_dict + nearest_interp: false + to_tensor: true + - _target_: AsDiscreted + keys: pred + argmax: true + - _target_: SaveImaged + keys: pred + meta_keys: pred_meta_dict + output_dir: "@output_dir" +handlers: +- _target_: CheckpointLoader + load_path: "$@bundle_root + '/models/model.pt'" + load_dict: + model: "@network" +- _target_: StatsHandler + iteration_log: false +evaluator: + _target_: SupervisedEvaluator + device: "@device" + val_data_loader: "@dataloader" + network: "@network" + inferer: "@inferer" + postprocessing: "@postprocessing" + val_handlers: "@handlers" + amp: true +evaluating: +- "$setattr(torch.backends.cudnn, 'benchmark', True)" +- "$@evaluator.run()" diff --git a/multi_organ_segmentation/configs/logging.conf b/multi_organ_segmentation/configs/logging.conf new file mode 100755 index 00000000..91c1a21c --- /dev/null +++ b/multi_organ_segmentation/configs/logging.conf @@ -0,0 +1,21 @@ +[loggers] +keys=root + +[handlers] +keys=consoleHandler + +[formatters] +keys=fullFormatter + +[logger_root] +level=INFO +handlers=consoleHandler + +[handler_consoleHandler] +class=StreamHandler +level=INFO +formatter=fullFormatter +args=(sys.stdout,) + +[formatter_fullFormatter] +format=%(asctime)s - %(name)s - %(levelname)s - %(message)s diff --git a/multi_organ_segmentation/configs/metadata.json b/multi_organ_segmentation/configs/metadata.json new file mode 100755 index 00000000..c9825995 --- /dev/null +++ b/multi_organ_segmentation/configs/metadata.json @@ -0,0 +1,99 @@ +{ + "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json", + "version": "0.3.5", + "changelog": { + "0.3.5": "restructure readme to match updated template", + "0.3.4": "correct typos", + "0.3.3": "update learning rate and readme", + "0.3.2": "update to use monai 1.0.1", + "0.3.1": "fix license Copyright error", + "0.3.0": "update license files", + "0.2.0": "unify naming", + "0.1.1": "fix data type issue in searching/training configurations", + "0.1.0": "complete the model package", + "0.0.1": "initialize the model package structure" + }, + "monai_version": "1.0.1", + "pytorch_version": "1.13.0", + "numpy_version": "1.21.2", + "optional_packages_version": { + "fire": "0.4.0", + "nibabel": "4.0.1", + "pytorch-ignite": "0.4.9" + }, + "task": "Multi-organ segmentation in abdominal CT", + "description": "DiNTS architectures for volumetric (3D) segmentation of the abdominal from CT image", + "authors": "Chen Shen, Holger R. Roth, Kazunari Misawa, Kensaku Mori", + "copyright": "", + "data_source": "Aichi Cancer Center, Japan", + "data_type": "nibabel", + "image_classes": "single channel data, intensity scaled to [0, 1]", + "label_classes": "eight channels data, 1 is artery, 2 is portal vein, 3 is liver, 4 is spleen, 5 is stomach, 6 is gallbladder, 7 is pancreas, 0 is everything else", + "pred_classes": "8 channels OneHot data, 1 is artery, 2 is portal vein, 3 is liver, 4 is spleen, 5 is stomach, 6 is gallbladder, 7 is pancreas, 0 is background", + "eval_metrics": { + "mean_dice": 0.88 + }, + "references": [ + "He, Y., Yang, D., Roth, H., Zhao, C. and Xu, D., 2021. Dints: Differentiable neural network topology search for 3d medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5841-5850).", + "Roth, H., Shen C, Oda H., Sugino T., Oda M., Hayashi Y., Misawa K., Mori K., 2018. A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation. International conference on medical image computing and computer-assisted intervention", + "Shen, C., Roth, H. R., Nath, V., Hayashi, Y., Oda, M., Misawa, K., Mori, K., 2022. Effective hyperparameter optimization with proxy data for multi-organ segmentation. In Medical Imaging 2022: Image Processing (Vol. 12032, pp. 200-206)" + ], + "network_data_format": { + "inputs": { + "image": { + "type": "image", + "format": "hounsfield", + "modality": "CT", + "num_channels": 1, + "spatial_shape": [ + 96, + 96, + 96 + ], + "dtype": "float64", + "value_range": [ + 0, + 1 + ], + "is_patch_data": true, + "channel_def": { + "0": "image" + } + } + }, + "outputs": { + "pred": { + "type": "image", + "format": "segmentation", + "num_channels": 8, + "spatial_shape": [ + 96, + 96, + 96 + ], + "dtype": "float32", + "value_range": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7 + ], + "is_patch_data": true, + "channel_def": { + "0": "background", + "1": "artery", + "2": "portal vein", + "3": "liver", + "4": "spleen", + "5": "stomach", + "6": "gallbladder", + "7": "pancreas" + } + } + } + } +} diff --git a/multi_organ_segmentation/configs/multi_gpu_train.yaml b/multi_organ_segmentation/configs/multi_gpu_train.yaml new file mode 100755 index 00000000..c03d67d2 --- /dev/null +++ b/multi_organ_segmentation/configs/multi_gpu_train.yaml @@ -0,0 +1,52 @@ +--- +device: "$torch.device(f'cuda:{dist.get_rank()}')" +network: + _target_: torch.nn.parallel.DistributedDataParallel + module: "$@network_def.to(@device)" + find_unused_parameters: true + device_ids: + - "@device" +optimizer#lr: "$0.025*dist.get_world_size()" +lr_scheduler#step_size: "$80*dist.get_world_size()" +train#handlers: + - _target_: LrScheduleHandler + lr_scheduler: "@lr_scheduler" + print_lr: true + - _target_: ValidationHandler + validator: "@validate#evaluator" + epoch_level: true + interval: "$10*dist.get_world_size()" + - _target_: StatsHandler + tag_name: train_loss + output_transform: "$monai.handlers.from_engine(['loss'], first=True)" + - _target_: TensorBoardStatsHandler + log_dir: "@output_dir" + tag_name: train_loss + output_transform: "$monai.handlers.from_engine(['loss'], first=True)" +train#trainer#max_epochs: "$400*dist.get_world_size()" +train#trainer#train_handlers: "$@train#handlers[: -2 if dist.get_rank() > 0 else None]" +validate#evaluator#val_handlers: "$None if dist.get_rank() > 0 else @validate#handlers" +training: +- "$import torch.distributed as dist" +- "$dist.init_process_group(backend='nccl')" +- "$torch.cuda.set_device(@device)" +- "$monai.utils.set_determinism(seed=123)" +- "$setattr(torch.backends.cudnn, 'benchmark', True)" +- "$@train#trainer.run()" +- "$dist.destroy_process_group()" +train_data_partition: "$monai.data.partition_dataset(data=@train_datalist, num_partitions=dist.get_world_size(), + shuffle=True, even_divisible=True,)[dist.get_rank()]" +train#dataset: + _target_: CacheDataset + data: "@train_data_partition" + transform: "@train#preprocessing" + cache_rate: 0.3 + num_workers: 4 +val_data_partition: "$monai.data.partition_dataset(data=@val_datalist, num_partitions=dist.get_world_size(), + shuffle=False, even_divisible=False,)[dist.get_rank()]" +validate#dataset: + _target_: CacheDataset + data: "@val_data_partition" + transform: "@validate#preprocessing" + cache_rate: 0.3 + num_workers: 4 diff --git a/multi_organ_segmentation/configs/search.yaml b/multi_organ_segmentation/configs/search.yaml new file mode 100755 index 00000000..f3fff4d6 --- /dev/null +++ b/multi_organ_segmentation/configs/search.yaml @@ -0,0 +1,275 @@ +--- +imports: + - "$from scipy import ndimage" +arch_ckpt_path: models +amp: true +data_file_base_dir: /workspace/data/msd/Task07_Pancreas +data_list_file_path: configs/dataset_0.json +determ: true +input_channels: 1 +learning_rate: 0.025 +learning_rate_arch: 0.001 +learning_rate_milestones: +- 0.4 +- 0.8 +num_images_per_batch: 1 +num_epochs: 1430 +num_epochs_per_validation: 100 +num_epochs_warmup: 715 +num_patches_per_image: 1 +num_sw_batch_size: 6 +output_classes: 3 +overlap_ratio: 0.625 +patch_size: +- 96 +- 96 +- 96 +patch_size_valid: +- 96 +- 96 +- 96 +ram_cost_factor: 0.8 +image_key: image +label_key: label +transform_train: + _target_: Compose + transforms: + - _target_: LoadImaged + keys: + - "@image_key" + - "@label_key" + - _target_: EnsureChannelFirstd + keys: + - "@image_key" + - "@label_key" + - _target_: Orientationd + keys: + - "@image_key" + - "@label_key" + axcodes: RAS + - _target_: Spacingd + keys: + - "@image_key" + - "@label_key" + pixdim: + - 1 + - 1 + - 1 + mode: + - bilinear + - nearest + align_corners: + - true + - true + - _target_: CastToTyped + keys: "@image_key" + dtype: "$torch.float32" + - _target_: ScaleIntensityRanged + keys: "@image_key" + a_min: -87 + a_max: 199 + b_min: 0 + b_max: 1 + clip: true + - _target_: CastToTyped + keys: + - "@image_key" + - "@label_key" + dtype: + - "$np.float16" + - "$np.uint8" + - _target_: CopyItemsd + keys: "@label_key" + times: 1 + names: + - label4crop + - _target_: Lambdad + keys: label4crop + func: "$lambda x, s=@output_classes: np.concatenate(tuple([ndimage.binary_dilation((x==_k).astype(x.dtype), iterations=48).astype(float) for _k in range(s)]), axis=0)" + overwrite: true + - _target_: EnsureTyped + keys: + - "@image_key" + - "@label_key" + - _target_: CastToTyped + keys: "@image_key" + dtype: "$torch.float32" + - _target_: SpatialPadd + keys: + - "@image_key" + - "@label_key" + - label4crop + spatial_size: "@patch_size" + mode: + - reflect + - constant + - constant + - _target_: RandCropByLabelClassesd + keys: + - "@image_key" + - "@label_key" + label_key: label4crop + num_classes: "@output_classes" + ratios: "$[1,] * @output_classes" + spatial_size: "@patch_size" + num_samples: "@num_patches_per_image" + - _target_: Lambdad + keys: label4crop + func: "$lambda x: 0" + - _target_: RandRotated + keys: + - "@image_key" + - "@label_key" + range_x: 0.3 + range_y: 0.3 + range_z: 0.3 + mode: + - bilinear + - nearest + prob: 0.2 + - _target_: RandZoomd + keys: + - "@image_key" + - "@label_key" + min_zoom: 0.8 + max_zoom: 1.2 + mode: + - trilinear + - nearest + align_corners: + - null + - null + prob: 0.16 + - _target_: RandGaussianSmoothd + keys: "@image_key" + sigma_x: + - 0.5 + - 1.15 + sigma_y: + - 0.5 + - 1.15 + sigma_z: + - 0.5 + - 1.15 + prob: 0.15 + - _target_: RandScaleIntensityd + keys: "@image_key" + factors: 0.3 + prob: 0.5 + - _target_: RandShiftIntensityd + keys: "@image_key" + offsets: 0.1 + prob: 0.5 + - _target_: RandGaussianNoised + keys: "@image_key" + std: 0.01 + prob: 0.15 + - _target_: RandFlipd + keys: + - "@image_key" + - "@label_key" + spatial_axis: 0 + prob: 0.5 + - _target_: RandFlipd + keys: + - "@image_key" + - "@label_key" + spatial_axis: 1 + prob: 0.5 + - _target_: RandFlipd + keys: + - "@image_key" + - "@label_key" + spatial_axis: 2 + prob: 0.5 + - _target_: CastToTyped + keys: + - "@image_key" + - "@label_key" + dtype: + - "$torch.float32" + - "$torch.uint8" + - _target_: ToTensord + keys: + - "@image_key" + - "@label_key" +transform_validation: + _target_: Compose + transforms: + - _target_: LoadImaged + keys: + - "@image_key" + - "@label_key" + - _target_: EnsureChannelFirstd + keys: + - "@image_key" + - "@label_key" + - _target_: Orientationd + keys: + - "@image_key" + - "@label_key" + axcodes: RAS + - _target_: Spacingd + keys: + - "@image_key" + - "@label_key" + pixdim: + - 1 + - 1 + - 1 + mode: + - bilinear + - nearest + align_corners: + - true + - true + - _target_: CastToTyped + keys: "@image_key" + dtype: "$torch.float32" + - _target_: ScaleIntensityRanged + keys: "@image_key" + a_min: -87 + a_max: 199 + b_min: 0 + b_max: 1 + clip: true + - _target_: CastToTyped + keys: + - "@image_key" + - "@label_key" + dtype: + - "$np.float16" + - "$np.uint8" + - _target_: CastToTyped + keys: + - "@image_key" + - "@label_key" + dtype: + - "$torch.float32" + - "$torch.uint8" + - _target_: ToTensord + keys: + - "@image_key" + - "@label_key" +loss: + _target_: DiceCELoss + include_background: false + to_onehot_y: true + softmax: true + squared_pred: true + batch: true + smooth_nr: 0.00001 + smooth_dr: 0.00001 +dints_space: + _target_: monai.networks.nets.TopologySearch + channel_mul: 0.5 + num_blocks: 12 + num_depths: 4 + use_downsample: true + device: "$torch.device('cuda')" +network: + _target_: monai.networks.nets.DiNTS + dints_space: "@dints_space" + in_channels: "@input_channels" + num_classes: "@output_classes" + use_downsample: true diff --git a/multi_organ_segmentation/configs/train.yaml b/multi_organ_segmentation/configs/train.yaml new file mode 100755 index 00000000..0156cd0d --- /dev/null +++ b/multi_organ_segmentation/configs/train.yaml @@ -0,0 +1,354 @@ +--- +imports: +- "$import glob" +- "$import json" +- "$import os" +- "$import ignite" +- "$from scipy import ndimage" +input_channels: 1 +output_classes: 8 +arch_ckpt_path: "$@bundle_root + '/models/search_code_18590.pt'" +arch_ckpt: "$torch.load(@arch_ckpt_path, map_location=torch.device('cuda'))" +bundle_root: "/work/" +ckpt_dir: "$@bundle_root + '/models'" +output_dir: "$@bundle_root + '/eval'" +dataset_dir: "/data/" +data_list_file_path: "$@bundle_root + '/configs/dataset_0.json'" +train_datalist: "$monai.data.load_decathlon_datalist(@data_list_file_path, data_list_key='training', + base_dir=@dataset_dir)" +val_datalist: "$monai.data.load_decathlon_datalist(@data_list_file_path, data_list_key='validation', + base_dir=@dataset_dir)" +device: "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')" +dints_space: + _target_: monai.networks.nets.TopologyInstance + channel_mul: 1 + num_blocks: 12 + num_depths: 4 + use_downsample: true + arch_code: + - "$@arch_ckpt['arch_code_a']" + - "$@arch_ckpt['arch_code_c']" + device: "$torch.device('cuda')" +network_def: + _target_: monai.networks.nets.DiNTS + dints_space: "@dints_space" + in_channels: "@input_channels" + num_classes: "@output_classes" + use_downsample: true + node_a: "$@arch_ckpt['node_a']" +network: "$@network_def.to(@device)" +loss: + _target_: DiceCELoss + include_background: false + to_onehot_y: true + softmax: true + squared_pred: true + batch: true + smooth_nr: 1.0e-05 + smooth_dr: 1.0e-05 +optimizer: + _target_: torch.optim.SGD + params: "$@network.parameters()" + momentum: 0.9 + weight_decay: 4.0e-05 + lr: 0.025 +lr_scheduler: + _target_: torch.optim.lr_scheduler.StepLR + optimizer: "@optimizer" + step_size: 80 + gamma: 0.5 +image_key: image +label_key: label +val_interval: 10 +train: + deterministic_transforms: + - _target_: LoadImaged + keys: + - "@image_key" + - "@label_key" + - _target_: EnsureChannelFirstd + keys: + - "@image_key" + - "@label_key" + - _target_: Orientationd + keys: + - "@image_key" + - "@label_key" + axcodes: RAS + - _target_: Spacingd + keys: + - "@image_key" + - "@label_key" + pixdim: + - 1 + - 1 + - 1 + mode: + - bilinear + - nearest + align_corners: + - true + - true + - _target_: CastToTyped + keys: "@image_key" + dtype: "$torch.float32" + - _target_: ScaleIntensityRanged + keys: "@image_key" + a_min: -500 + a_max: 500 + b_min: 0 + b_max: 1 + clip: true + - _target_: CastToTyped + keys: + - "@image_key" + - "@label_key" + dtype: + - "$np.float16" + - "$np.uint8" + - _target_: CopyItemsd + keys: "@label_key" + times: 1 + names: + - label4crop + - _target_: Lambdad + keys: label4crop + func: "$lambda x, s=@output_classes: np.concatenate(tuple([ndimage.binary_dilation((x==_k).astype(x.dtype), + iterations=48).astype(float) for _k in range(s)]), axis=0)" + overwrite: true + - _target_: EnsureTyped + keys: + - "@image_key" + - "@label_key" + - _target_: CastToTyped + keys: "@image_key" + dtype: "$torch.float32" + - _target_: SpatialPadd + keys: + - "@image_key" + - "@label_key" + - label4crop + spatial_size: + - 96 + - 96 + - 96 + mode: + - reflect + - constant + - constant + random_transforms: + - _target_: RandCropByLabelClassesd + keys: + - "@image_key" + - "@label_key" + label_key: label4crop + num_classes: "@output_classes" + ratios: "$[1,] * @output_classes" + spatial_size: + - 96 + - 96 + - 96 + num_samples: 1 + - _target_: Lambdad + keys: label4crop + func: "$lambda x: 0" + - _target_: RandRotated + keys: + - "@image_key" + - "@label_key" + range_x: 0.3 + range_y: 0.3 + range_z: 0.3 + mode: + - bilinear + - nearest + prob: 0.2 + - _target_: RandZoomd + keys: + - "@image_key" + - "@label_key" + min_zoom: 0.8 + max_zoom: 1.2 + mode: + - trilinear + - nearest + align_corners: + - true + - + prob: 0.16 + - _target_: RandGaussianSmoothd + keys: "@image_key" + sigma_x: + - 0.5 + - 1.15 + sigma_y: + - 0.5 + - 1.15 + sigma_z: + - 0.5 + - 1.15 + prob: 0.15 + - _target_: RandScaleIntensityd + keys: "@image_key" + factors: 0.3 + prob: 0.5 + - _target_: RandShiftIntensityd + keys: "@image_key" + offsets: 0.1 + prob: 0.5 + - _target_: RandGaussianNoised + keys: "@image_key" + std: 0.01 + prob: 0.15 + - _target_: RandFlipd + keys: + - "@image_key" + - "@label_key" + spatial_axis: 0 + prob: 0.5 + - _target_: RandFlipd + keys: + - "@image_key" + - "@label_key" + spatial_axis: 1 + prob: 0.5 + - _target_: RandFlipd + keys: + - "@image_key" + - "@label_key" + spatial_axis: 2 + prob: 0.5 + - _target_: CastToTyped + keys: + - "@image_key" + - "@label_key" + dtype: + - "$torch.float32" + - "$torch.uint8" + - _target_: ToTensord + keys: + - "@image_key" + - "@label_key" + preprocessing: + _target_: Compose + transforms: "$@train#deterministic_transforms + @train#random_transforms" + dataset: + _target_: CacheDataset + data: "@train_datalist" + transform: "@train#preprocessing" + cache_rate: 0.125 + num_workers: 4 + dataloader: + _target_: DataLoader + dataset: "@train#dataset" + batch_size: 2 + shuffle: true + num_workers: 4 + inferer: + _target_: SimpleInferer + postprocessing: + _target_: Compose + transforms: + - _target_: Activationsd + keys: pred + softmax: true + - _target_: AsDiscreted + keys: + - pred + - label + argmax: + - true + - false + to_onehot: "@output_classes" + handlers: + - _target_: LrScheduleHandler + lr_scheduler: "@lr_scheduler" + print_lr: true + - _target_: ValidationHandler + validator: "@validate#evaluator" + epoch_level: true + interval: "@val_interval" + - _target_: StatsHandler + tag_name: train_loss + output_transform: "$monai.handlers.from_engine(['loss'], first=True)" + - _target_: TensorBoardStatsHandler + log_dir: "@output_dir" + tag_name: train_loss + output_transform: "$monai.handlers.from_engine(['loss'], first=True)" + key_metric: + train_accuracy: + _target_: ignite.metrics.Accuracy + output_transform: "$monai.handlers.from_engine(['pred', 'label'])" + trainer: + _target_: SupervisedTrainer + max_epochs: 400 + device: "@device" + train_data_loader: "@train#dataloader" + network: "@network" + loss_function: "@loss" + optimizer: "@optimizer" + inferer: "@train#inferer" + postprocessing: "@train#postprocessing" + key_train_metric: "@train#key_metric" + train_handlers: "@train#handlers" + amp: true +validate: + preprocessing: + _target_: Compose + transforms: "%train#deterministic_transforms" + dataset: + _target_: CacheDataset + data: "@val_datalist" + transform: "@validate#preprocessing" + cache_rate: 0.125 + dataloader: + _target_: DataLoader + dataset: "@validate#dataset" + batch_size: 1 + shuffle: false + num_workers: 4 + inferer: + _target_: SlidingWindowInferer + roi_size: + - 96 + - 96 + - 96 + sw_batch_size: 6 + overlap: 0.625 + postprocessing: "%train#postprocessing" + handlers: + - _target_: StatsHandler + iteration_log: false + - _target_: TensorBoardStatsHandler + log_dir: "@output_dir" + iteration_log: false + - _target_: CheckpointSaver + save_dir: "@ckpt_dir" + save_dict: + model: "@network" + save_key_metric: true + key_metric_filename: model.pt + key_metric: + val_mean_dice: + _target_: MeanDice + include_background: false + output_transform: "$monai.handlers.from_engine(['pred', 'label'])" + additional_metrics: + val_accuracy: + _target_: ignite.metrics.Accuracy + output_transform: "$monai.handlers.from_engine(['pred', 'label'])" + evaluator: + _target_: SupervisedEvaluator + device: "@device" + val_data_loader: "@validate#dataloader" + network: "@network" + inferer: "@validate#inferer" + postprocessing: "@validate#postprocessing" + key_val_metric: "@validate#key_metric" + additional_metrics: "@validate#additional_metrics" + val_handlers: "@validate#handlers" + amp: true +training: +- "$monai.utils.set_determinism(seed=123)" +- "$setattr(torch.backends.cudnn, 'benchmark', True)" +- "$@train#trainer.run()" diff --git a/multi_organ_segmentation/docs/README.md b/multi_organ_segmentation/docs/README.md new file mode 100755 index 00000000..7006d386 --- /dev/null +++ b/multi_organ_segmentation/docs/README.md @@ -0,0 +1,137 @@ +# Model Overview +A neural architecture search algorithm for volumetric (3D) segmentation of the pancreas and pancreatic tumor from CT image. This model is trained using the neural network model from the neural architecture search algorithm, DiNTS [1]. + +![image](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_net_arch_search_segmentation_workflow_4-1.png) + +## Data +The training dataset is the Panceas Task from the Medical Segmentation Decathalon. Users can find more details on the datasets at http://medicaldecathlon.com/. + +- Target: Liver and tumour +- Modality: Portal venous phase CT +- Size: 420 3D volumes (282 Training +139 Testing) +- Source: Memorial Sloan Kettering Cancer Center +- Challenge: Label unbalance with large (background), medium (pancreas) and small (tumour) structures. + +### Preprocessing +The data list/split can be created with the script `scripts/prepare_datalist.py`. + +``` +python scripts/prepare_datalist.py --path /path-to-Task07_Pancreas/ --output configs/dataset_0.json +``` + +## Training configuration +The training was performed with at least 16GB-memory GPUs. + +Actual Model Input: 96 x 96 x 96 + +### Neural Architecture Search Configuration +The neural architecture search was performed with the following: + +- AMP: True +- Optimizer: SGD +- Initial Learning Rate: 0.025 +- Loss: DiceCELoss + +### Optimial Architecture Training Configuration +The training was performed with the following: + +- AMP: True +- Optimizer: SGD +- (Initial) Learning Rate: 0.025 +- Loss: DiceCELoss +- Note: If out-of-memory or program crash occurs while caching the data set, please change the cache\_rate in CacheDataset to a lower value in the range (0, 1). + +The segmentation of pancreas region is formulated as the voxel-wise 3-class classification. Each voxel is predicted as either foreground (pancreas body, tumour) or background. And the model is optimized with gradient descent method minimizing soft dice loss and cross-entropy loss between the predicted mask and ground truth segmentation. + +### Input +One channel +- CT image + +### Output +Three channels +- Label 2: pancreatic tumor +- Label 1: pancreas +- Label 0: everything else + +## Performance +Dice score is used for evaluating the performance of the model. This model achieves a mean dice score of 0.62. + +#### Training Loss +The loss over 3200 epochs (the bright curve is smoothed, and the dark one is the actual curve) + +![Training loss over 3200 epochs (the bright curve is smoothed, and the dark one is the actual curve)](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_net_arch_search_segmentation_train_4-2.png) + +#### Validation Dice +The mean dice score over 3200 epochs (the bright curve is smoothed, and the dark one is the actual curve) + +![Validation mean dice score over 3200 epochs (the bright curve is smoothed, and the dark one is the actual curve)](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_net_arch_search_segmentation_validation_4-2.png) + +### Searched Architecture Visualization +Users can install Graphviz for visualization of searched architectures (needed in custom/decode_plot.py). The edges between nodes indicate global structure, and numbers next to edges represent different operations in the cell searching space. An example of searched architecture is shown as follows: + +![Example of Searched Architecture](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_net_arch_search_segmentation_searched_arch_example.png) + +## MONAI Bundle Commands +In addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file. + +For more details usage instructions, visit the [MONAI Bundle Configuration Page](https://docs.monai.io/en/latest/config_syntax.html). + +#### Execute model searching: + +``` +python -m scripts.search run --config_file configs/search.yaml +``` + +#### Execute multi-GPU model searching (recommended): + +``` +torchrun --nnodes=1 --nproc_per_node=8 -m scripts.search run --config_file configs/search.yaml +``` + +#### Execute training: + +``` +python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.yaml --logging_file configs/logging.conf +``` + +#### Override the `train` config to execute multi-GPU training: + +``` +torchrun --nnodes=1 --nproc_per_node=2 -m monai.bundle run training --meta_file configs/metadata.json --config_file "['configs/train.yaml','configs/multi_gpu_train.yaml']" --logging_file configs/logging.conf +``` + +#### Override the `train` config to execute evaluation with the trained model: + +``` +python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.yaml','configs/evaluate.yaml']" --logging_file configs/logging.conf +``` + +#### Execute inference: + +``` +python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.yaml --logging_file configs/logging.conf +``` + +#### Export checkpoint for TorchScript + +``` +python -m monai.bundle ckpt_export network_def --filepath models/model.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.yaml +``` + +# References +[1] He, Y., Yang, D., Roth, H., Zhao, C. and Xu, D., 2021. Dints: Differentiable neural network topology search for 3d medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5841-5850). + +# License +Copyright (c) MONAI Consortium + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. diff --git a/multi_organ_segmentation/docs/license.txt b/multi_organ_segmentation/docs/license.txt new file mode 100755 index 00000000..e69de29b diff --git a/multi_organ_segmentation/large_files.yml b/multi_organ_segmentation/large_files.yml new file mode 100755 index 00000000..b20ee9eb --- /dev/null +++ b/multi_organ_segmentation/large_files.yml @@ -0,0 +1,13 @@ +large_files: + - path: "models/search_code_18590.pt" + url: "https://drive.google.com/uc?export=download&id=1G2YZo1HKkvf5sMvaaRQeZp_YA9GDlvL-" + hash_val: "55dc7fe4bee4a93d25a1dd329dfdd159" + hash_type: "md5" + - path: "models/model.pt" + url: "https://drive.google.com/uc?export=download&id=1kH0yTyiXUNqdYXpnSXI2p5-vFwDYoCzl" + hash_val: "2a167107a34ac588130ec47509b21b9e" + hash_type: "md5" + - path: "models/model.ts" + url: "https://drive.google.com/uc?export=download&id=1qwV99IfYvLzpjsHgfrlciqDp8yQ5uLXs" + hash_val: "5c56d20b4e74bd7f730882f9f691b408" + hash_type: "md5" From d5214ed04d77dc24c0d320220cd63502d29240a2 Mon Sep 17 00:00:00 2001 From: Chen Shen Date: Wed, 14 Dec 2022 22:23:22 +0900 Subject: [PATCH 02/34] Update the readme --- multi_organ_segmentation/docs/README.md | 152 ++++++++++-------------- 1 file changed, 64 insertions(+), 88 deletions(-) diff --git a/multi_organ_segmentation/docs/README.md b/multi_organ_segmentation/docs/README.md index 7006d386..277b8181 100755 --- a/multi_organ_segmentation/docs/README.md +++ b/multi_organ_segmentation/docs/README.md @@ -1,137 +1,113 @@ -# Model Overview -A neural architecture search algorithm for volumetric (3D) segmentation of the pancreas and pancreatic tumor from CT image. This model is trained using the neural network model from the neural architecture search algorithm, DiNTS [1]. +# Multi-organ segmentation in abdominal CT -![image](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_net_arch_search_segmentation_workflow_4-1.png) +### **Authors** -## Data -The training dataset is the Panceas Task from the Medical Segmentation Decathalon. Users can find more details on the datasets at http://medicaldecathlon.com/. +Chen Shen1, Holger R. Roth2, Kazunari Misawa3, Kensaku Mori1 -- Target: Liver and tumour -- Modality: Portal venous phase CT -- Size: 420 3D volumes (282 Training +139 Testing) -- Source: Memorial Sloan Kettering Cancer Center -- Challenge: Label unbalance with large (background), medium (pancreas) and small (tumour) structures. +1. Nagoya University, Japan -### Preprocessing -The data list/split can be created with the script `scripts/prepare_datalist.py`. +2. NVIDIA Corporation, USA -``` -python scripts/prepare_datalist.py --path /path-to-Task07_Pancreas/ --output configs/dataset_0.json -``` - -## Training configuration -The training was performed with at least 16GB-memory GPUs. +3. Aichi Cancer Center, Japan -Actual Model Input: 96 x 96 x 96 +### **Tags** -### Neural Architecture Search Configuration -The neural architecture search was performed with the following: +Segmentation, Multi-organ, Abdominal -- AMP: True -- Optimizer: SGD -- Initial Learning Rate: 0.025 -- Loss: DiceCELoss +## **Model Description** -### Optimial Architecture Training Configuration -The training was performed with the following: +This model uses the DiNTS model architecture searched on MSD Pancreas [1] and re-trained for multi-organ segmentation from abdominal CT images [2,3]. -- AMP: True -- Optimizer: SGD -- (Initial) Learning Rate: 0.025 -- Loss: DiceCELoss -- Note: If out-of-memory or program crash occurs while caching the data set, please change the cache\_rate in CacheDataset to a lower value in the range (0, 1). +## **Data** -The segmentation of pancreas region is formulated as the voxel-wise 3-class classification. Each voxel is predicted as either foreground (pancreas body, tumour) or background. And the model is optimized with gradient descent method minimizing soft dice loss and cross-entropy loss between the predicted mask and ground truth segmentation. +This model was trained on an abdominal CT dataset in portal venous phase from Aichi Cancer Center in Japan. The dataset was split into traininig, validation and testing with 300, 60 and 60 cases, respectively. -### Input -One channel -- CT image +The labels{"0": "background","1": "artery","2": "portal vein","3": "liver", -### Output -Three channels -- Label 2: pancreatic tumor -- Label 1: pancreas -- Label 0: everything else +"4": "spleen","5": "stomach","6": "gallbladder","7": "pancreas"} -## Performance -Dice score is used for evaluating the performance of the model. This model achieves a mean dice score of 0.62. +## **Scores** -#### Training Loss -The loss over 3200 epochs (the bright curve is smoothed, and the dark one is the actual curve) +This model achieves the following Dice score on the validation data (our own split from the training dataset): -![Training loss over 3200 epochs (the bright curve is smoothed, and the dark one is the actual curve)](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_net_arch_search_segmentation_train_4-2.png) +Mean Dice = 0.88 -#### Validation Dice -The mean dice score over 3200 epochs (the bright curve is smoothed, and the dark one is the actual curve) +## **commands example** -![Validation mean dice score over 3200 epochs (the bright curve is smoothed, and the dark one is the actual curve)](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_net_arch_search_segmentation_validation_4-2.png) +Execute training: -### Searched Architecture Visualization -Users can install Graphviz for visualization of searched architectures (needed in custom/decode_plot.py). The edges between nodes indicate global structure, and numbers next to edges represent different operations in the cell searching space. An example of searched architecture is shown as follows: +``` +python -m monai.bundle run training \ -![Example of Searched Architecture](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_net_arch_search_segmentation_searched_arch_example.png) +    --meta_file configs/metadata.json \ -## MONAI Bundle Commands -In addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file. +    --config_file configs/train.yaml \ -For more details usage instructions, visit the [MONAI Bundle Configuration Page](https://docs.monai.io/en/latest/config_syntax.html). +    --logging_file configs/logging.conf +``` -#### Execute model searching: +Execute multi-GPU training ``` -python -m scripts.search run --config_file configs/search.yaml -``` +torchrun --nnodes=1 --nproc_per_node=4 \ -#### Execute multi-GPU model searching (recommended): +-m monai.bundle run training 、 -``` -torchrun --nnodes=1 --nproc_per_node=8 -m scripts.search run --config_file configs/search.yaml -``` +--meta_file configs/metadata.json \ -#### Execute training: +--config_file "['configs/train.yaml','configs/multi_gpu_train.yaml']" \ -``` -python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.yaml --logging_file configs/logging.conf +--logging_file configs/logging.conf ``` -#### Override the `train` config to execute multi-GPU training: +Execute inference: ``` -torchrun --nnodes=1 --nproc_per_node=2 -m monai.bundle run training --meta_file configs/metadata.json --config_file "['configs/train.yaml','configs/multi_gpu_train.yaml']" --logging_file configs/logging.conf -``` +python -m monai.bundle run evaluating \ -#### Override the `train` config to execute evaluation with the trained model: +    --meta_file configs/metadata.json \ +    --config_file configs/inference.yaml \ + +    --logging_file configs/logging.conf + +```   + +Override the train config to execute evaluation with the trained model: ``` -python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.yaml','configs/evaluate.yaml']" --logging_file configs/logging.conf -``` -#### Execute inference: +python -m monai.bundle run evaluating \ + +    --meta_file configs/metadata.json \ + +    --config_file "['configs/train.yaml','configs/evaluate.yaml']" \ + +    --logging_file configs/logging.conf ``` -python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.yaml --logging_file configs/logging.conf +Export checkpoint to TorchScript file: ``` -#### Export checkpoint for TorchScript +python -m monai.bundle ckpt_export network_def \ + +    --filepath models/model.ts \ + +    --ckpt_file models/model.pt \ + +    --meta_file configs/metadata.json \ + +    --config_file configs/inference.yaml ``` -python -m monai.bundle ckpt_export network_def --filepath models/model.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.yaml -``` -# References +## **References** + [1] He, Y., Yang, D., Roth, H., Zhao, C. and Xu, D., 2021. Dints: Differentiable neural network topology search for 3d medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5841-5850). -# License -Copyright (c) MONAI Consortium +​ -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at +[2] Roth, Holger R., et al. "A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation." International conference on medical image computing and computer-assisted intervention. Springer, Cham, 2018. - http://www.apache.org/licenses/LICENSE-2.0 +​ -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. +[3] Shen, Chen, et al. "Effective hyperparameter optimization with proxy data for multi-organ segmentation." Medical Imaging 2022: Image Processing. Vol. 12032. SPIE, 2022. From c3f0014f970079e5cf10263a0cb100e90066ed39 Mon Sep 17 00:00:00 2001 From: Chen Shen Date: Wed, 14 Dec 2022 22:28:20 +0900 Subject: [PATCH 03/34] Update the readme --- multi_organ_segmentation/docs/README.md | 26 ++++++++++++------------- 1 file changed, 12 insertions(+), 14 deletions(-) diff --git a/multi_organ_segmentation/docs/README.md b/multi_organ_segmentation/docs/README.md index 277b8181..c82d96b2 100755 --- a/multi_organ_segmentation/docs/README.md +++ b/multi_organ_segmentation/docs/README.md @@ -46,7 +46,7 @@ python -m monai.bundle run training \     --logging_file configs/logging.conf ``` -Execute multi-GPU training +Execute multi-GPU training with 4 GPUs: ``` torchrun --nnodes=1 --nproc_per_node=4 \ @@ -70,12 +70,11 @@ python -m monai.bundle run evaluating \     --config_file configs/inference.yaml \     --logging_file configs/logging.conf - -```   +``` Override the train config to execute evaluation with the trained model: -``` +``` python -m monai.bundle run evaluating \     --meta_file configs/metadata.json \ @@ -84,21 +83,20 @@ python -m monai.bundle run evaluating \     --logging_file configs/logging.conf -``` -Export checkpoint to TorchScript file: -``` - -python -m monai.bundle ckpt_export network_def \ -    --filepath models/model.ts \ +``` -    --ckpt_file models/model.pt \ +Export checkpoint to TorchScript file: -    --meta_file configs/metadata.json \ +``` +python -m monai.bundle ckpt_export network_def \ + --filepath models/model.ts \ + --ckpt_file models/model.pt \ + --meta_file configs/metadata.json \ + --config_file configs/inference.yaml +``` -    --config_file configs/inference.yaml -``` ## **References** From 28b57bc2150675ad1a4133fcd1dc7cad41c02830 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 14 Dec 2022 13:33:13 +0000 Subject: [PATCH 04/34] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- multi_organ_segmentation/configs/dataset_0.json | 2 +- multi_organ_segmentation/configs/inference.yaml | 2 +- multi_organ_segmentation/docs/README.md | 8 ++++---- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/multi_organ_segmentation/configs/dataset_0.json b/multi_organ_segmentation/configs/dataset_0.json index 9bad22dc..35e85281 100755 --- a/multi_organ_segmentation/configs/dataset_0.json +++ b/multi_organ_segmentation/configs/dataset_0.json @@ -1685,4 +1685,4 @@ "image": "ACC_g467h/ACC_g467h-data.nii.gz" } ] -} \ No newline at end of file +} diff --git a/multi_organ_segmentation/configs/inference.yaml b/multi_organ_segmentation/configs/inference.yaml index aadfe094..2c029ada 100755 --- a/multi_organ_segmentation/configs/inference.yaml +++ b/multi_organ_segmentation/configs/inference.yaml @@ -6,7 +6,7 @@ input_channels: 1 output_classes: 8 arch_ckpt_path: "$@bundle_root + '/models/search_code_18590.pt'" arch_ckpt: "$torch.load(@arch_ckpt_path, map_location=torch.device('cuda'))" -bundle_root: "/work/" +bundle_root: "/work/" output_dir: "$@bundle_root + '/eval'" dataset_dir: "/data/" data_list_file_path: "$@bundle_root + '/configs/dataset_0.json'" diff --git a/multi_organ_segmentation/docs/README.md b/multi_organ_segmentation/docs/README.md index c82d96b2..ffe79908 100755 --- a/multi_organ_segmentation/docs/README.md +++ b/multi_organ_segmentation/docs/README.md @@ -89,10 +89,10 @@ python -m monai.bundle run evaluating \ Export checkpoint to TorchScript file: ``` -python -m monai.bundle ckpt_export network_def \ - --filepath models/model.ts \ - --ckpt_file models/model.pt \ - --meta_file configs/metadata.json \ +python -m monai.bundle ckpt_export network_def \ + --filepath models/model.ts \ + --ckpt_file models/model.pt \ + --meta_file configs/metadata.json \ --config_file configs/inference.yaml ``` From 2ccc77d477510e873caf4187b12593ddb7a61193 Mon Sep 17 00:00:00 2001 From: Chen Shen Date: Tue, 20 Dec 2022 16:11:02 +0900 Subject: [PATCH 05/34] modify the permissions --- multi_organ_segmentation/configs/metadata.json | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/multi_organ_segmentation/configs/metadata.json b/multi_organ_segmentation/configs/metadata.json index c9825995..1fdeb566 100755 --- a/multi_organ_segmentation/configs/metadata.json +++ b/multi_organ_segmentation/configs/metadata.json @@ -1,16 +1,7 @@ { "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json", - "version": "0.3.5", + "version": "0.0.1", "changelog": { - "0.3.5": "restructure readme to match updated template", - "0.3.4": "correct typos", - "0.3.3": "update learning rate and readme", - "0.3.2": "update to use monai 1.0.1", - "0.3.1": "fix license Copyright error", - "0.3.0": "update license files", - "0.2.0": "unify naming", - "0.1.1": "fix data type issue in searching/training configurations", - "0.1.0": "complete the model package", "0.0.1": "initialize the model package structure" }, "monai_version": "1.0.1", From 1c16bf2b4bbd15d03d78ea1aeec206e4088cf662 Mon Sep 17 00:00:00 2001 From: Chen Shen Date: Tue, 20 Dec 2022 18:45:43 +0900 Subject: [PATCH 06/34] update perimission --- multi_organ_segmentation/docs/README.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) mode change 100755 => 100644 multi_organ_segmentation/docs/README.md diff --git a/multi_organ_segmentation/docs/README.md b/multi_organ_segmentation/docs/README.md old mode 100755 new mode 100644 From 33481817052a346523c8e666c7d496a12a1283ba Mon Sep 17 00:00:00 2001 From: Chen Shen Date: Tue, 20 Dec 2022 18:46:16 +0900 Subject: [PATCH 07/34] update perimission --- multi_organ_segmentation/docs/README.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) mode change 100644 => 100755 multi_organ_segmentation/docs/README.md diff --git a/multi_organ_segmentation/docs/README.md b/multi_organ_segmentation/docs/README.md old mode 100644 new mode 100755 From b8cb47840ecffc8d2a0837c80d36931e982d1bc2 Mon Sep 17 00:00:00 2001 From: Chen Shen Date: Tue, 20 Dec 2022 18:53:17 +0900 Subject: [PATCH 08/34] update permissions --- multi_organ_segmentation/configs/dataset_0.json | 0 multi_organ_segmentation/configs/evaluate.yaml | 0 multi_organ_segmentation/configs/inference.yaml | 0 multi_organ_segmentation/configs/logging.conf | 0 multi_organ_segmentation/configs/metadata.json | 0 multi_organ_segmentation/configs/multi_gpu_train.yaml | 0 multi_organ_segmentation/configs/search.yaml | 0 multi_organ_segmentation/configs/train.yaml | 0 8 files changed, 0 insertions(+), 0 deletions(-) mode change 100755 => 100644 multi_organ_segmentation/configs/dataset_0.json mode change 100755 => 100644 multi_organ_segmentation/configs/evaluate.yaml mode change 100755 => 100644 multi_organ_segmentation/configs/inference.yaml mode change 100755 => 100644 multi_organ_segmentation/configs/logging.conf mode change 100755 => 100644 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a/multi_organ_segmentation/configs/multi_gpu_train.yaml b/multi_organ_segmentation/configs/multi_gpu_train.yaml old mode 100755 new mode 100644 diff --git a/multi_organ_segmentation/configs/search.yaml b/multi_organ_segmentation/configs/search.yaml old mode 100755 new mode 100644 diff --git a/multi_organ_segmentation/configs/train.yaml b/multi_organ_segmentation/configs/train.yaml old mode 100755 new mode 100644 From 2465fab81c39be560e4d6bc344b0dfadd81f490b Mon Sep 17 00:00:00 2001 From: Chen Shen Date: Tue, 20 Dec 2022 18:54:46 +0900 Subject: [PATCH 09/34] update permissions --- multi_organ_segmentation/configs/dataset_0.json | 0 multi_organ_segmentation/configs/evaluate.yaml | 0 multi_organ_segmentation/configs/inference.yaml | 0 multi_organ_segmentation/configs/logging.conf | 0 multi_organ_segmentation/configs/metadata.json | 0 multi_organ_segmentation/configs/multi_gpu_train.yaml | 0 multi_organ_segmentation/configs/search.yaml | 0 multi_organ_segmentation/configs/train.yaml | 0 8 files changed, 0 insertions(+), 0 deletions(-) mode change 100644 => 100755 multi_organ_segmentation/configs/dataset_0.json mode change 100644 => 100755 multi_organ_segmentation/configs/evaluate.yaml mode change 100644 => 100755 multi_organ_segmentation/configs/inference.yaml mode change 100644 => 100755 multi_organ_segmentation/configs/logging.conf mode change 100644 => 100755 multi_organ_segmentation/configs/metadata.json mode change 100644 => 100755 multi_organ_segmentation/configs/multi_gpu_train.yaml mode change 100644 => 100755 multi_organ_segmentation/configs/search.yaml mode change 100644 => 100755 multi_organ_segmentation/configs/train.yaml diff --git a/multi_organ_segmentation/configs/dataset_0.json b/multi_organ_segmentation/configs/dataset_0.json old mode 100644 new mode 100755 diff --git a/multi_organ_segmentation/configs/evaluate.yaml b/multi_organ_segmentation/configs/evaluate.yaml old mode 100644 new mode 100755 diff --git a/multi_organ_segmentation/configs/inference.yaml b/multi_organ_segmentation/configs/inference.yaml old mode 100644 new mode 100755 diff --git a/multi_organ_segmentation/configs/logging.conf b/multi_organ_segmentation/configs/logging.conf old mode 100644 new mode 100755 diff --git a/multi_organ_segmentation/configs/metadata.json b/multi_organ_segmentation/configs/metadata.json old mode 100644 new mode 100755 diff --git a/multi_organ_segmentation/configs/multi_gpu_train.yaml b/multi_organ_segmentation/configs/multi_gpu_train.yaml old mode 100644 new mode 100755 diff --git a/multi_organ_segmentation/configs/search.yaml b/multi_organ_segmentation/configs/search.yaml old mode 100644 new mode 100755 diff --git a/multi_organ_segmentation/configs/train.yaml b/multi_organ_segmentation/configs/train.yaml old mode 100644 new mode 100755 From 8cedb4f3afb3322faaff5cc7757b3f7e88e1393c Mon Sep 17 00:00:00 2001 From: Chen Shen Date: Tue, 20 Dec 2022 21:23:42 +0900 Subject: [PATCH 10/34] update permissions --- multi_organ_segmentation/LICENSE | 0 multi_organ_segmentation/large_files.yml | 0 2 files changed, 0 insertions(+), 0 deletions(-) mode change 100755 => 100644 multi_organ_segmentation/LICENSE mode change 100755 => 100644 multi_organ_segmentation/large_files.yml diff --git a/multi_organ_segmentation/LICENSE b/multi_organ_segmentation/LICENSE old mode 100755 new mode 100644 diff --git a/multi_organ_segmentation/large_files.yml b/multi_organ_segmentation/large_files.yml old mode 100755 new mode 100644 From 4341bffe12d4b703c2bfb901cb5d59d99ef7b0fd Mon Sep 17 00:00:00 2001 From: Chen Shen Date: Tue, 20 Dec 2022 21:25:40 +0900 Subject: [PATCH 11/34] update --- multi_organ_segmentation/docs/README.md | 0 multi_organ_segmentation/docs/license.txt | 0 2 files changed, 0 insertions(+), 0 deletions(-) mode change 100755 => 100644 multi_organ_segmentation/docs/README.md mode change 100755 => 100644 multi_organ_segmentation/docs/license.txt diff --git a/multi_organ_segmentation/docs/README.md b/multi_organ_segmentation/docs/README.md old mode 100755 new mode 100644 diff --git a/multi_organ_segmentation/docs/license.txt b/multi_organ_segmentation/docs/license.txt old mode 100755 new mode 100644 From aa8c895580560861b2a1051a220af3abe8126dbb Mon Sep 17 00:00:00 2001 From: Chen Shen Date: Tue, 20 Dec 2022 21:27:18 +0900 Subject: [PATCH 12/34] update --- multi_organ_segmentation/configs/dataset_0.json | 0 multi_organ_segmentation/configs/evaluate.yaml | 0 multi_organ_segmentation/configs/inference.yaml | 0 multi_organ_segmentation/configs/logging.conf | 0 multi_organ_segmentation/configs/metadata.json | 0 multi_organ_segmentation/configs/multi_gpu_train.yaml | 0 multi_organ_segmentation/configs/search.yaml | 0 multi_organ_segmentation/configs/train.yaml | 0 8 files changed, 0 insertions(+), 0 deletions(-) mode change 100755 => 100644 multi_organ_segmentation/configs/dataset_0.json mode change 100755 => 100644 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b/multi_organ_segmentation/configs/logging.conf old mode 100755 new mode 100644 diff --git a/multi_organ_segmentation/configs/metadata.json b/multi_organ_segmentation/configs/metadata.json old mode 100755 new mode 100644 diff --git a/multi_organ_segmentation/configs/multi_gpu_train.yaml b/multi_organ_segmentation/configs/multi_gpu_train.yaml old mode 100755 new mode 100644 diff --git a/multi_organ_segmentation/configs/search.yaml b/multi_organ_segmentation/configs/search.yaml old mode 100755 new mode 100644 diff --git a/multi_organ_segmentation/configs/train.yaml b/multi_organ_segmentation/configs/train.yaml old mode 100755 new mode 100644 From 0d8ccce1483073096e98ef3365554598923d4ad0 Mon Sep 17 00:00:00 2001 From: Chen Shen Date: Tue, 27 Dec 2022 16:21:01 +0900 Subject: [PATCH 13/34] update the data specification on README --- multi_organ_segmentation/docs/README.md | 37 +++++++------------------ 1 file changed, 10 insertions(+), 27 deletions(-) diff --git a/multi_organ_segmentation/docs/README.md b/multi_organ_segmentation/docs/README.md index ffe79908..a37cd24a 100644 --- a/multi_organ_segmentation/docs/README.md +++ b/multi_organ_segmentation/docs/README.md @@ -16,19 +16,19 @@ Segmentation, Multi-organ, Abdominal ## **Model Description** -This model uses the DiNTS model architecture searched on MSD Pancreas [1] and re-trained for multi-organ segmentation from abdominal CT images [2,3]. +This model uses the DiNTS model architecture searched on [Medical Segmentation Decathlon](http://medicaldecathlon.com/) Pancreas [1] and re-trained for multi-organ segmentation from abdominal CT images [2,3]. ## **Data** -This model was trained on an abdominal CT dataset in portal venous phase from Aichi Cancer Center in Japan. The dataset was split into traininig, validation and testing with 300, 60 and 60 cases, respectively. +This model was trained on an abdominal CT dataset in portal venous phase collected from Aichi Cancer Center in Japan. Since this is a private dataset, similar models can be trained using other public multi-organ datasets like [BTCV](https://www.synapse.org/#!Synapse:syn3193805/wiki/89480). -The labels{"0": "background","1": "artery","2": "portal vein","3": "liver", +For this bundle, we split the 420 cases into training, validation and testing with 300, 60 and 60 cases, respectively. -"4": "spleen","5": "stomach","6": "gallbladder","7": "pancreas"} +The labels are {"0": "background","1": "artery","2": "portal vein","3": "liver","4": "spleen","5": "stomach","6": "gallbladder","7": "pancreas"} ## **Scores** -This model achieves the following Dice score on the validation data (our own split from the training dataset): +This model achieves the following Dice score on the validation data (our own split from the whole dataset): Mean Dice = 0.88 @@ -38,37 +38,27 @@ Execute training: ``` python -m monai.bundle run training \ -     --meta_file configs/metadata.json \ -     --config_file configs/train.yaml \ -     --logging_file configs/logging.conf ``` Execute multi-GPU training with 4 GPUs: ``` -torchrun --nnodes=1 --nproc_per_node=4 \ - --m monai.bundle run training 、 - ---meta_file configs/metadata.json \ - ---config_file "['configs/train.yaml','configs/multi_gpu_train.yaml']" \ - ---logging_file configs/logging.conf +torchrun --nnodes=4 --nproc_per_node=4 \ + -m monai.bundle run training \ + --meta_file configs/metadata.json \ + --config_file "['configs/train.yaml','configs/multi_gpu_train.yaml']" \ + --logging_file configs/logging.conf ``` Execute inference: ``` python -m monai.bundle run evaluating \ -     --meta_file configs/metadata.json \ -     --config_file configs/inference.yaml \ -     --logging_file configs/logging.conf ``` @@ -76,14 +66,9 @@ Override the train config to execute evaluation with the trained model: ``` python -m monai.bundle run evaluating \ -     --meta_file configs/metadata.json \ -     --config_file "['configs/train.yaml','configs/evaluate.yaml']" \ -     --logging_file configs/logging.conf - - ``` Export checkpoint to TorchScript file: @@ -103,9 +88,7 @@ python -m monai.bundle ckpt_export network_def \ [1] He, Y., Yang, D., Roth, H., Zhao, C. and Xu, D., 2021. Dints: Differentiable neural network topology search for 3d medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5841-5850). ​ - [2] Roth, Holger R., et al. "A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation." International conference on medical image computing and computer-assisted intervention. Springer, Cham, 2018. - ​ [3] Shen, Chen, et al. "Effective hyperparameter optimization with proxy data for multi-organ segmentation." Medical Imaging 2022: Image Processing. Vol. 12032. SPIE, 2022. From b476e10af9813764eaad06e5483dbc87179e1992 Mon Sep 17 00:00:00 2001 From: Chen Shen Date: Tue, 27 Dec 2022 16:23:28 +0900 Subject: [PATCH 14/34] remove the license --- multi_organ_segmentation/docs/license.txt | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 multi_organ_segmentation/docs/license.txt diff --git a/multi_organ_segmentation/docs/license.txt b/multi_organ_segmentation/docs/license.txt deleted file mode 100644 index e69de29b..00000000 From a593cbf3b7cb89ef481ae0b00de034824fe38b9c Mon Sep 17 00:00:00 2001 From: Chen Shen Date: Thu, 5 Jan 2023 16:10:30 +0900 Subject: [PATCH 15/34] update README --- multi_organ_segmentation/docs/README.md | 14 +++++++++++++- .../docs/output_example.png | Bin 0 -> 897641 bytes 2 files changed, 13 insertions(+), 1 deletion(-) create mode 100644 multi_organ_segmentation/docs/output_example.png diff --git a/multi_organ_segmentation/docs/README.md b/multi_organ_segmentation/docs/README.md index a37cd24a..6c58cab3 100644 --- a/multi_organ_segmentation/docs/README.md +++ b/multi_organ_segmentation/docs/README.md @@ -24,7 +24,19 @@ This model was trained on an abdominal CT dataset in portal venous phase collect For this bundle, we split the 420 cases into training, validation and testing with 300, 60 and 60 cases, respectively. -The labels are {"0": "background","1": "artery","2": "portal vein","3": "liver","4": "spleen","5": "stomach","6": "gallbladder","7": "pancreas"} +## **Output** +8 channels + +- 0: Background +- 1: Artery +- 2: Portal vein +- 3: Liver +- 4: Spleen +- 5: Stomach +- 6: Gallbladder +- 7: Pancreas + +![alt用テキスト](output_example.png) ## **Scores** diff --git a/multi_organ_segmentation/docs/output_example.png b/multi_organ_segmentation/docs/output_example.png new file mode 100644 index 0000000000000000000000000000000000000000..a7d6bf5c9a6045a06ce13f6947c2ba51aa0b1e9c GIT binary patch literal 897641 zcmXtfby(By_x?twNJys$jP6b;>23xL5D@8ZnNrd!EsX9M4WkDN$Ow_{bRZH+OaJ!% 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