A pre-trained model using AutoML feature for volumetric (3D) segmentation of the prostate central gland and peripheral zone from the multimodal MR (T2, ADC).
This model is trained using the runnerup [1] awarded pipeline of the "Medical Segmentation Decathlon Challenge 2018" using the AHnet architecture [2] with 25 training image pairs and 7 validation images.
Training Data Source: Task05_Prostate.tar from http://medicaldecathlon.com/ The data was converted to resolution 1mm x 1mm x 1mm for training, using the following command.
nvmidl-dataconvert -d ${SOURCE_IMAGE_ROOT} -r 1 -s .nii.gz -e .nii.gz -o ${DESTINATION_IMAGE_ROOT}
The training was performed on 12GB-memory GPUs with the command automl.sh, which ran Clara Train AutoML to perform parameter search [3].
Training Graph Input Shape: 96 x 96 x 32
Input: 2 channel MR image
Output: 2 channels: Label 1: prostate; Label 0: everything else
This model achieve the following Dice score on the validation data (our own split from the training dataset):
In order to access this model please apply for general access:
https://developer.nvidia.com/clara
This model is usable only as part of 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.
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.
[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.
[3] Yang, Dong, et al. "Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019. https://link.springer.com/chapter/10.1007/978-3-030-32245-8_1.