MONAI Pancreas CT DiNTS Segmentation

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].

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)

Validation Dice

The mean dice score over 3200 epochs (the bright curve is smoothed, and the dark one is the actual curve)

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:

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.

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).

Disclaimer

This training and inference pipeline was developed by NVIDIA. It is based on a model developed by NVIDIA researchers. This software has not been cleared or approved by FDA or any regulatory agency. MONAI pre-trained models are for developmental purposes only and cannot be used directly for clinical procedures.

Disclaimer

This training and inference pipeline was developed by NVIDIA. It is based on a model developed by NVIDIA researchers. This software has not been cleared or approved by FDA or any regulatory agency. MONAI pre-trained models are for developmental purposes only and cannot be used directly for clinical procedures.

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.