Description

0.7.4

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

This model is trained using the runnerup [1] awarded pipeline of the “Medical Segmentation Decathlon Challenge 2018” using the AHnet architecture [2] with 32 training images and 9 validation images.

Data

The training dataset is Task09_Spleen.tar from http://medicaldecathlon.com/.

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

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

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

Training configuration

The training was performed with command train_2gpu.sh, which required 12GB-memory GPUs.

Training Graph Input Shape: dynamic

Actual Model Input: 96 x 96 x 96

Input and output formats

Input: 1 channel CT image

Output: 2 channels: Label 1: spleen; Label 0: everything else

Scores

This model achieve the following Dice score on the validation data (our own split from the training dataset):

  1. Spleen: 0.951

In order to access this model please apply for general access:

https://developer.nvidia.com/clara

This model is usable only as part of Transfer Learning & Annotation Tools in Clara Train SDK container. You can download the model from NGC registry as described in Getting Started Guide

This is an example, not to be used for diagnostic purposes

[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.

[2] Liu, Siqi, et al. “3d anisotropic hybrid network: Transferring convolutional features from 2d images to 3d anisotropic volumes.” International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2018. https://arxiv.org/abs/1711.08580.

© Copyright 2018-2020, NVIDIA Corporation. All rights reserved. Last updated on Feb 1, 2023.