A pre-trained model for volumetric (3D) segmentation of the COVID-19 lesion from CT images.
Note: The 4.1 version of this model is only compatible with the 4.1 version of the Clara Train SDK container
The model is trained using a 3D SegResNet .
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).
This model was trained on a global dataset with a large experimental cohort collected from across the globe. The CT volumes of 919 independent subjects are provided by NIH with experts’ lesion region annotations.
Dice score is used for evaluating the performance of the model. On the test set, the trained model achieved score of 0.7109 for lesion.
A graph showing the training loss for 2000 epochs (46000 iterations).
A graph showing the validation mean Dice for 2000 epochs (46000 iterations).
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 and arbitary spacing
Augmentation for training:
Output: 2 channels
Inference is performed on 3D volumes 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.
 Myronenko, A., 2018, September. 3D MRI brain tumor segmentation using autoencoder regularization. In International MICCAI Brainlesion Workshop (pp. 311-320). Springer, Cham. https://arxiv.org/pdf/1810.11654.pdf
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