Symlink README.md

README.md

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+# Model Overview
+A pre-trained model for volumetric (3D) segmentation of brain tumor subregions from multimodal MRIs based on BraTS 2018 data. The whole pipeline is modified from [clara_pt_brain_mri_segmentation](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/med/models/clara_pt_brain_mri_segmentation).
+
+## Workflow
+
+The model is trained to segment 3 nested subregions of primary brain tumors (gliomas): the "enhancing tumor" (ET), the "tumor core" (TC), the "whole tumor" (WT) based on 4 aligned input MRI scans (T1c, T1, T2, FLAIR).
+- The ET is described by areas that show hyper intensity in T1c when compared to T1, but also when compared to "healthy" white matter in T1c.
+- The TC describes the bulk of the tumor, which is what is typically resected. The TC entails the ET, as well as the necrotic (fluid-filled) and the non-enhancing (solid) parts of the tumor.
+-  The WT describes the complete extent of the disease, as it entails the TC and the peritumoral edema (ED), which is typically depicted by hyper-intense signal in FLAIR.
+
+## Data
+
+The training data is from the [Multimodal Brain Tumor Segmentation Challenge (BraTS) 2018](https://www.med.upenn.edu/sbia/brats2018/data.html).
+
+- Target: 3 tumor subregions
+- Task: Segmentation
+- Modality: MRI
+- Size: 285 3D volumes (4 channels each)
+
+The provided labelled data was partitioned, based on our own split, into training (200 studies), validation (42 studies) and testing (43 studies) datasets.
+
+Please run `scripts/prepare_datalist.py` to produce the data list. The command is like:
+
+```
+python scripts/prepare_datalist.py --path your-brats18-dataset-path
+```
+
+## Training configuration
+
+This model utilized a similar approach described in 3D MRI brain tumor segmentation
+using autoencoder regularization, which was a winning method in BraTS2018 [1]. The training was performed with the following:
+
+- GPU: At least 16GB of GPU memory.
+- Actual Model Input: 224 x 224 x 144
+- AMP: True
+- Optimizer: Adam
+- Learning Rate: 1e-4
+- Loss: DiceLoss
+
+## Input
+
+Input: 4 channel MRI (4 aligned MRIs T1c, T1, T2, FLAIR at 1x1x1 mm)
+
+1. Normalizing to unit std with zero mean
+2. Randomly cropping to (224, 224, 144)
+3. Randomly spatial flipping
+4. Randomly scaling and shifting intensity of the volume
+
+## Output
+
+Output: 3 channels
+- Label 0: TC tumor subregion
+- Label 1: WT tumor subregion
+- Label 2: ET tumor subregion
+
+## Model Performance
+
+The achieved Dice scores on the validation data are:
+- Tumor core (TC): 0.8559
+- Whole tumor (WT): 0.9026
+- Enhancing tumor (ET): 0.7905
+- Average: 0.8518
+
+## commands example
+
+Execute training:
+
+```
+python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf
+```
+
+Override the `train` config to execute multi-GPU training:
+
+```
+torchrun --standalone --nnodes=1 --nproc_per_node=8 -m monai.bundle run training --meta_file configs/metadata.json --config_file "['configs/train.json','configs/multi_gpu_train.json']" --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.json','configs/evaluate.json']" --logging_file configs/logging.conf
+```
+
+Execute inference:
+
+```
+python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf
+```
+
+# Disclaimer
+
+This is an example, not to be used for diagnostic purposes.
+
+# References
+
+[1] Myronenko, Andriy. "3D MRI brain tumor segmentation using autoencoder regularization." International MICCAI Brainlesion Workshop. Springer, Cham, 2018. https://arxiv.org/abs/1810.11654.

docs/README.md

@@ -1,96 +0,0 @@
-# Model Overview
-A pre-trained model for volumetric (3D) segmentation of brain tumor subregions from multimodal MRIs based on BraTS 2018 data. The whole pipeline is modified from [clara_pt_brain_mri_segmentation](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/med/models/clara_pt_brain_mri_segmentation).
-
-## Workflow
-
-The model is trained to segment 3 nested subregions of primary brain tumors (gliomas): the "enhancing tumor" (ET), the "tumor core" (TC), the "whole tumor" (WT) based on 4 aligned input MRI scans (T1c, T1, T2, FLAIR).
-- The ET is described by areas that show hyper intensity in T1c when compared to T1, but also when compared to "healthy" white matter in T1c.
-- The TC describes the bulk of the tumor, which is what is typically resected. The TC entails the ET, as well as the necrotic (fluid-filled) and the non-enhancing (solid) parts of the tumor.
--  The WT describes the complete extent of the disease, as it entails the TC and the peritumoral edema (ED), which is typically depicted by hyper-intense signal in FLAIR.
-
-## Data
-
-The training data is from the [Multimodal Brain Tumor Segmentation Challenge (BraTS) 2018](https://www.med.upenn.edu/sbia/brats2018/data.html).
-
-- Target: 3 tumor subregions
-- Task: Segmentation
-- Modality: MRI
-- Size: 285 3D volumes (4 channels each)
-
-The provided labelled data was partitioned, based on our own split, into training (200 studies), validation (42 studies) and testing (43 studies) datasets.
-
-Please run `scripts/prepare_datalist.py` to produce the data list. The command is like:
-
-```
-python scripts/prepare_datalist.py --path your-brats18-dataset-path
-```
-
-## Training configuration
-
-This model utilized a similar approach described in 3D MRI brain tumor segmentation
-using autoencoder regularization, which was a winning method in BraTS2018 [1]. The training was performed with the following:
-
-- GPU: At least 16GB of GPU memory.
-- Actual Model Input: 224 x 224 x 144
-- AMP: True
-- Optimizer: Adam
-- Learning Rate: 1e-4
-- Loss: DiceLoss
-
-## Input
-
-Input: 4 channel MRI (4 aligned MRIs T1c, T1, T2, FLAIR at 1x1x1 mm)
-
-1. Normalizing to unit std with zero mean
-2. Randomly cropping to (224, 224, 144)
-3. Randomly spatial flipping
-4. Randomly scaling and shifting intensity of the volume
-
-## Output
-
-Output: 3 channels
-- Label 0: TC tumor subregion
-- Label 1: WT tumor subregion
-- Label 2: ET tumor subregion
-
-## Model Performance
-
-The achieved Dice scores on the validation data are:
-- Tumor core (TC): 0.8559
-- Whole tumor (WT): 0.9026
-- Enhancing tumor (ET): 0.7905
-- Average: 0.8518
-
-## commands example
-
-Execute training:
-
-```
-python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf
-```
-
-Override the `train` config to execute multi-GPU training:
-
-```
-torchrun --standalone --nnodes=1 --nproc_per_node=8 -m monai.bundle run training --meta_file configs/metadata.json --config_file "['configs/train.json','configs/multi_gpu_train.json']" --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.json','configs/evaluate.json']" --logging_file configs/logging.conf
-```
-
-Execute inference:
-
-```
-python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf
-```
-
-# Disclaimer
-
-This is an example, not to be used for diagnostic purposes.
-
-# References
-
-[1] Myronenko, Andriy. "3D MRI brain tumor segmentation using autoencoder regularization." International MICCAI Brainlesion Workshop. Springer, Cham, 2018. https://arxiv.org/abs/1810.11654.

docs/README.md

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+/home/daniel/co/brats_mri_segmentation/README.md