SeBRe (Segmenting Brain Regions) is a high-throughput Mask R-CNN [1]-based toolbox to generate brain atlas through deep learning by instance segmentation of complex brain regions.
Nature Machine Intelligence article: Asim Iqbal, et al. Developing a brain atlas through deep learning. Nature Machine Intelligence
arXiv preprint: SeBRe
The Colab version of SeBRe is available here to train and test on your custom dataset:
git clone https://github.com/itsasimiqbal/SeBRe.git
https://drive.google.com/open?id=1g_894zM_mSJGfudzzfnw-ZpN-rQ4qQdz
You would need DATASETSubmit.zip, myDATASET.zip and SeBRe_FINAL_WEIGHTS.h5.zip to run the code in your machine. If you'd like to use the mouse and human brain atlas datasets used in our paper then also download and unzip SeBRe_Datasets.zip. Once you've downloaded your files then unzip them in your folder by running the following commands:
unzip DATASETSubmit.zip
unzip myDATASET.zip
unzip SeBRe_FINAL_WEIGHTS.h5.zip
Following are the Python/library versions on which the SeBRe 1.0 code is tested to work fine:
- Python (3.5.2)
- Tensorflow (1.6.0)
- Keras (2.1.6)
- skimage (0.13.0)
- Numpy (1.13.3)
- Scipy (1.2.2)
The block diagram of our system is demonstrated below:
Run the notebook SeBRe_FINAL.ipynb to reproduce the results in SeBRe's paper. Make sure to install the necessary libraries in your machine before running the code. A step-by-step explanation of feature processing in SeBRe is provided in SeBRe_feature_processing.ipynb notebook.
1. Collect images of brain regions (or sub-regions) and place each of them in a separate folder with the following naming convention e.g. section_img_0.jpg, section_img_1.jpg, ...
2. Draw ground-truth (binary) masks on Regions of Interest (ROIs) e.g. cortex, hippocampus, etc. using an annotation software such as Napari [2] and assign a unique color code to each ROI. In the figure below, mouse brain section is shown before (a) and after (d) annotation by human expert. A zoomed-in examples of cortex (b) and hindbrain (c) are shown to precisely match the boundaries of masks with the corresponding ROIs.
3. Run the notebook custom_dataset_create.ipynb to generate the binary (black and white) masks to train SeBRe deep neural network (DNN). The notebook will generate the file names in SeBRe-readable format, place them in the brain region corresponding folders e.g. section_masks_0/section_masks_0_m_1.png, sections_masks_0_m_2.png, ...
4. Modify and run the notebook SeBRe_training.ipynb to train the SeBRe DNN on your (custom) dataset.
5. Modify and run the notebook SeBRe_FINAL.ipynb to test the SeBRe DNN on your (custom) dataset.
[1] https://arxiv.org/abs/1703.06870
If you use any part of this code for your work, please cite the following:
Asim Iqbal, Romesa Khan, and Theofanis Karayannis. "Developing a brain atlas through deep learning."
Nature Machine Intelligence 1.6 (2019): 277-287.