restructure readme to match updated template

README.md

@@ -5,16 +5,21 @@ tags:
 library_name: monai
 license: apache-2.0
 ---
-# Description
-A pre-trained model for volumetric (3D) segmentation of the spleen from CT image.
-
 # Model Overview
+A pre-trained model for volumetric (3D) segmentation of the spleen from CT images.
+
 This model is trained using the runner-up [1] awarded pipeline of the "Medical Segmentation Decathlon Challenge 2018" using the UNet architecture [2] with 32 training images and 9 validation images.
 
-![image](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_spleen_ct_segmentation_workflow.png)
+![model workflow](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_spleen_ct_segmentation_workflow.png)
 
 ## Data
-The training dataset is Task09_Spleen.tar from http://medicaldecathlon.com/.
+The training dataset is the Spleen Task from the Medical Segmentation Decathalon. Users can find more details on the datasets at http://medicaldecathlon.com/.
+
+- Target: Spleen
+- Modality: CT
+- Size: 61 3D volumes (41 Training + 20 Testing)
+- Source: Memorial Sloan Kettering Cancer Center
+- Challenge: Large-ranging foreground size
 
 ## Training configuration
 The segmentation of spleen region is formulated as the voxel-wise binary classification. Each voxel is predicted as either foreground (spleen) or background. And the model is optimized with gradient descent method minimizing Dice + cross entropy loss between the predicted mask and ground truth segmentation.
@@ -28,73 +33,61 @@ The training was performed with the following:
 - Learning Rate: 1e-4
 - Loss: DiceCELoss
 
-Pre-processing transforms:
-
-1. Convert data to channel-first
-2. Resample to resolution 1.5 x 1.5 x 2 mm
-3. Scale intensity
-4. Cropping foreground surrounding regions
-5. Cropping random fixed sized regions of size [96,96,96] with the center being a foreground or background voxel at ratio 1 : 1
-6. Randomly shifting intensity of the volume
+### Input
+One channel
+- CT image
 
-## Input and output formats
-Input: 1 channel CT image
+### Output
+Two channels
+- Label 1: spleen
+- Label 0: everything else
 
-Output: 2 channels: Label 1: spleen; Label 0: everything else
+## Performance
+Dice score is used for evaluating the performance of the model. This model achieves a mean dice score of 0.96.
 
-## Scores
-This model achieves the following Dice score on the validation data (our own split from the training dataset):
+#### Training Loss
+![A graph showing the training loss over 1260 epochs (10080 iterations).](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_spleen_ct_segmentation_train_2.png)
 
-Mean Dice = 0.96
+#### Validation Dice
+![A graph showing the validation mean Dice over 1260 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_spleen_ct_segmentation_val_2.png)
 
-## Training Performance
-A graph showing the training loss over 1260 epochs (10080 iterations).
+## 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.
 
-![](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_spleen_ct_segmentation_train_2.png) <br>
+For more details usage instructions, visit the [MONAI Bundle Configuration Page](https://docs.monai.io/en/latest/config_syntax.html).
 
-## Validation Performance
-A graph showing the validation mean Dice over 1260 epochs.
-
-![](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_spleen_ct_segmentation_val_2.png) <br>
-
-
-## commands example
-Execute training:
+#### 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:
+#### Override the `train` config to execute multi-GPU training:
 
 ```
 torchrun --standalone --nnodes=1 --nproc_per_node=2 -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
 ```
 
-Please note that the distributed training related options depend on the actual running environment, thus you may need to remove `--standalone`, modify `--nnodes` or do some other necessary changes according to the machine you used.
-Please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) for more details.
+Please note that the distributed training-related options depend on the actual running environment; thus, users may need to remove `--standalone`, modify `--nnodes`, or do some other necessary changes according to the machine used. For more details, please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).
 
-Override the `train` config to execute evaluation with the trained model:
+#### 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
 ```
 
-Override the `train` config and `evaluate` config to execute multi-GPU evaluation:
+#### Override the `train` config and `evaluate` config to execute multi-GPU evaluation:
 
 ```
 torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json','configs/multi_gpu_evaluate.json']" --logging_file configs/logging.conf
 ```
 
-Execute inference:
+#### 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] Xia, Yingda, et al. "3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training." arXiv preprint arXiv:1811.12506 (2018). https://arxiv.org/abs/1811.12506.
 

configs/metadata.json

@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.3.6",
+    "version": "0.3.7",
     "changelog": {
+        "0.3.7": "restructure readme to match updated template",
         "0.3.6": "enhance readme with details of model training",
         "0.3.5": "update to use monai 1.0.1",
         "0.3.4": "enhance readme on commands example",

docs/README.md

@@ -1,13 +1,18 @@
-# Description
-A pre-trained model for volumetric (3D) segmentation of the spleen from CT image.
-
 # Model Overview
+A pre-trained model for volumetric (3D) segmentation of the spleen from CT images.
+
 This model is trained using the runner-up [1] awarded pipeline of the "Medical Segmentation Decathlon Challenge 2018" using the UNet architecture [2] with 32 training images and 9 validation images.
 
-![image](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_spleen_ct_segmentation_workflow.png)
+![model workflow](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_spleen_ct_segmentation_workflow.png)
 
 ## Data
-The training dataset is Task09_Spleen.tar from http://medicaldecathlon.com/.
+The training dataset is the Spleen Task from the Medical Segmentation Decathalon. Users can find more details on the datasets at http://medicaldecathlon.com/.
+
+- Target: Spleen
+- Modality: CT
+- Size: 61 3D volumes (41 Training + 20 Testing)
+- Source: Memorial Sloan Kettering Cancer Center
+- Challenge: Large-ranging foreground size
 
 ## Training configuration
 The segmentation of spleen region is formulated as the voxel-wise binary classification. Each voxel is predicted as either foreground (spleen) or background. And the model is optimized with gradient descent method minimizing Dice + cross entropy loss between the predicted mask and ground truth segmentation.
@@ -21,73 +26,61 @@ The training was performed with the following:
 - Learning Rate: 1e-4
 - Loss: DiceCELoss
 
-Pre-processing transforms:
-
-1. Convert data to channel-first
-2. Resample to resolution 1.5 x 1.5 x 2 mm
-3. Scale intensity
-4. Cropping foreground surrounding regions
-5. Cropping random fixed sized regions of size [96,96,96] with the center being a foreground or background voxel at ratio 1 : 1
-6. Randomly shifting intensity of the volume
+### Input
+One channel
+- CT image
 
-## Input and output formats
-Input: 1 channel CT image
+### Output
+Two channels
+- Label 1: spleen
+- Label 0: everything else
 
-Output: 2 channels: Label 1: spleen; Label 0: everything else
+## Performance
+Dice score is used for evaluating the performance of the model. This model achieves a mean dice score of 0.96.
 
-## Scores
-This model achieves the following Dice score on the validation data (our own split from the training dataset):
+#### Training Loss
+![A graph showing the training loss over 1260 epochs (10080 iterations).](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_spleen_ct_segmentation_train_2.png)
 
-Mean Dice = 0.96
+#### Validation Dice
+![A graph showing the validation mean Dice over 1260 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_spleen_ct_segmentation_val_2.png)
 
-## Training Performance
-A graph showing the training loss over 1260 epochs (10080 iterations).
+## 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.
 
-![](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_spleen_ct_segmentation_train_2.png) <br>
+For more details usage instructions, visit the [MONAI Bundle Configuration Page](https://docs.monai.io/en/latest/config_syntax.html).
 
-## Validation Performance
-A graph showing the validation mean Dice over 1260 epochs.
-
-![](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_spleen_ct_segmentation_val_2.png) <br>
-
-
-## commands example
-Execute training:
+#### 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:
+#### Override the `train` config to execute multi-GPU training:
 
 ```
 torchrun --standalone --nnodes=1 --nproc_per_node=2 -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
 ```
 
-Please note that the distributed training related options depend on the actual running environment, thus you may need to remove `--standalone`, modify `--nnodes` or do some other necessary changes according to the machine you used.
-Please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) for more details.
+Please note that the distributed training-related options depend on the actual running environment; thus, users may need to remove `--standalone`, modify `--nnodes`, or do some other necessary changes according to the machine used. For more details, please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).
 
-Override the `train` config to execute evaluation with the trained model:
+#### 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
 ```
 
-Override the `train` config and `evaluate` config to execute multi-GPU evaluation:
+#### Override the `train` config and `evaluate` config to execute multi-GPU evaluation:
 
 ```
 torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json','configs/multi_gpu_evaluate.json']" --logging_file configs/logging.conf
 ```
 
-Execute inference:
+#### 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] Xia, Yingda, et al. "3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training." arXiv preprint arXiv:1811.12506 (2018). https://arxiv.org/abs/1811.12506.