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clara_pt_spleen_ct_segmentation

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Description

A pre-trained model for volumetric (3D) segmentation of the spleen from CT image.

Publisher

NVIDIA

Use Case

Segmentation

Framework

PyTorch

Latest Version

4.1

Modified

March 25, 2022

Size

36.97 MB

Model Overview

A pre-trained model for volumetric (3D) segmentation of the spleen from CT image.

Note: The 4.1 version of this model is only compatible with the 4.1 version of the Clara Train SDK container

Model Architecture

This model is trained using the UNet architecture [1].

Segmentation Workflow

Training

The training was performed with the following:

  • Script: train.sh
  • GPU: At least 16GB of GPU memory.
  • Actual Model Input: 96 x 96 x 96
  • AMP: True
  • Optimizer: Adam
  • Learning Rate: 2e-4
  • Loss: DiceLoss

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 soft dice loss [2] between the predicted mask and ground truth segmentation.

Dataset

The training data is from the Medical Decathlon.

  • Target: Spleen
  • Task: Segmentation
  • Modality: CT
  • Size: 61 3D volumes (31 Training, 4 Validation, 6 Testing, 20 challenge Test Set)
  • Challenge: Large ranging foreground size

The training dataset contains 31 images while the validation and testing datasets contain 4 and 6 images respectively. The challenge test set contains 20 images.

Data Preparation

The data must be converted to 1mm resolution before training:

medl-dataconvert -d ${SOURCE_IMAGE_ROOT} -r 1 -s .nii.gz -e .nii.gz -o ${DESTINATION_IMAGE_ROOT}

NOTE: To match the default setting, we suggest that ${DESTINATION_IMAGE_ROOT} match DATA_ROOT as defined in environment.json in this MMAR's config folder.

Performance

Dice score is used for evaluating the performance of the model. The trained model achieved a Dice score of 0.9644 on the test set (reproduce the results by changing VAL_DATALIST_KEY in environment.json to 'testing' and then run validate.sh).

Training

A graph showing the training loss over 1260 epochs (10080 iterations).

A graph showing the training loss over 1260 epochs (10080 iterations).

Validation

A graph showing the validation mean Dice over 1260 epochs.

A graph showing the validation mean Dice over 1260 epochs.

How to Use this Model

The model was validated with NVIDIA hardware and software. For hardware, the model can run on any NVIDIA GPU with memory greater than 16 GB. For software, this model is usable only as part of Transfer Learning & Annotation Tools in Clara Train SDK container. Find out more about Clara Train at the Clara Train Collections on NGC.

Full instructions for the training and validation workflow can be found in our documentation.

Input

Input: 1 channel CT image with intensity in HU and fixed spacing 1 x 1 x 1mm

Preprocessing:

  1. Converting to channel first
  2. Normalizing to unit std with zero mean
  3. Cropping foreground surrounding regions

Augmentation for training:

  1. Cropping random fixed sized regions of size 96 x 96 x 96 with the center being a foreground or background voxel at ratio 1 : 1
  2. Randomly shifting intensity of the volume

Output

Output: 2 channels

  • Label 0: background
  • Label 1: foreground (spleen)

Sliding-window Inference

Inference is performed in a sliding window manner with a specified stride.

Limitations

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

References

[1] Çiçek, Özgün, et al. "3D U-Net: learning dense volumetric segmentation from sparse annotation." International conference on medical image computing and computer-assisted intervention. Springer, Cham, 2016. https://arxiv.org/abs/1606.06650.

[2] Milletari, Fausto, Nassir Navab, and Seyed-Ahmad Ahmadi. "V-net: Fully convolutional neural networks for volumetric medical image segmentation." 2016 fourth international conference on 3D vision (3DV). IEEE, 2016. https://arxiv.org/abs/1606.04797.

License

End User License Agreement is included with the product. Licenses are also available along with the model application zip file. By pulling and using the Clara Train SDK container and downloading models, you accept the terms and conditions of these licenses.