Merge branch 'main' into um/model_image_readme

README.md

@@ -10,7 +10,7 @@ This repository contains two models, one for spinal cord (SC) segmentation and o
 ## Dependencies
 
 - [SCT](https://spinalcordtoolbox.com/) commit: 7fd2ea718751dd858840c3823c0830a910d9777c
-- [ivadomed](https://ivadomed.org) commit: XXX
+- [ivadomed](https://ivadomed.org) commit: git-master-93ba03df76d229d8a190c694f757da6c00efa545
 
 ## Clone this repository
 
@@ -20,25 +20,78 @@ git clone https://github.com/ivadomed/model_seg_ms_mp2rage.git
 
 ## Get the data
 
-- data.neuro.polymtl.ca:basel-mp2rage
-- Commit: 88c506eb9855d6a0cb29a2e95c3283b8fd0a8099
+- [email protected]:datasets/basel-mp2rage
+- Commit: 93110d8ebb65398dcc6e4528bf548eb7828332f1
+
+### Example calls to get the data
+
+~~~
+git clone [email protected]:datasets/basel-mp2rage
+cd basel-mp2rage
+git annex get .
+cd ..
+~~~
 
 ## Prepare the data
 
 The data need to be preprocessed before training. The preprocessing command is:
 
 ~~~
-sct_run_batch -script <PATH_TO_REPOSITORY>/preprocessing/preprocess_data.sh -path-data <PATH_TO_DATA>/basel-mp2rage/ -path-output <PATH_OUTPUT> -jobs <JOBS>
+sct_run_batch -script <PATH_TO_REPOSITORY>/preprocessing/preprocess_data.sh -path-data <PATH_TO_DATA>/basel-mp2rage/ -path-output basel-mp2rage-preprocessed -jobs <JOBS>
 ~~~
 
 This command will create a `data_processed_scseg` folder for the SC segmentation task and a 
-`data_processed_lesionseg` folder for the lesion segmentation task inside the `<PATH_OUTPUT>` 
+`data_processed_lesionseg` folder for the lesion segmentation task inside the `basel-mp2rage-preprocessed` 
 you specified. Each of these two folders contain only the required files for their respective task.
 
 After running the preprocessing, you can also run the quality-control (QC) script:
 ```
-python preprocessing/qc_preprocess.py -s <PATH_OUTPUT>
+python preprocessing/qc_preprocess.py -s basel-mp2rage-preprocessed
+```
+which i) logs resolutions and sizes for each subject image (both raw and cropped) for data exploration, 
+ii) performs basic shape checks for spinal cord (SC) cropped images and ground-truths (GTs), and 
+most importantly iii) checks if the dilated SC mask leaves out any lesions from the GT of each rater.
+
+## Training
+
+Spinal cord segmentation training was carried out with
+```
+ivadomed --train -c config/seg_sc.json
+```
+
+Lesion segmentation training was carried out with
+```
+ivadomed --train -c config/seg_lesion.json
+```
+
+## Get trained models
+
+```
+cp -r ~/duke/temp/uzay/saved_models_basel/seg_sc_output .
+cp -r ~/duke/temp/uzay/saved_models_basel/seg_lesion_output .
+```
+
+## Performance evaluation
+
+To test a spinal cord segmentation model run
+```
+ivadomed --test -c config/seg_sc.json
+```
+
+To test a lesion segmentation model independently on the two rater's annotations
+```
+ivadomed --test -c config/test_on_rater1.json
+ivadomed --test -c config/test_on_rater2.json
+```
+
+## Segment a dataset
+
+To run inference with spinal cord segmentation model on a dataset run
+```
+ivadomed --segment -c config/seg_sc.json --path-data <PATH_DATA>
+```
+
+To run inference with lesion segmentation model on a dataset run
+```
+ivadomed --segment -c config/seg_lesion.json --path-data <PATH_DATA>
 ```
-which i) logs resolutions and sizes for each SC-cropped subject image for data exploration, 
-ii) performs basic shape checks for SC-cropped images and ground-truths (GTs), and most importantly 
-iii) checks if the dilated spinal-cord (SC) mask leaves out any lesions from the GT of each rater.