A pre-trained model for volumetric (3D) segmentation of the pancreas and tumour from CT image.
This model is trained using the U-Net architecture .
The segmentation of spleen 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  between the predicted mask and ground truth segmentation.
The training was performed with the following:
If out-of-memory or program crash occurs while caching the data set, please change the
CacheDataset to a lower value in the range (0, 1).
The training data is from the Medical Decathlon.
The training dataset contains 196 images while the validation and testing datasets contain 56 and 29 images respectively.
Dice score is used for evaluating the performance of the model. The trained model achieved average testing Dice score 0.5599 over 29 volumes (0.7661 for class 1, and 0.3538 for class 2).
Training loss over 1000 epochs.
Validation mean dice score over 1000 epochs.
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: 1 channel CT image with intensity in HU
Augmentation for training:
Output: 3 channels
Inference is performed in a sliding window manner with a specified stride.
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.
 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.
 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.
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