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CatalogModelsclara_mri_fed_learning_seg_brain_tumors_br16_t1c2tc_no_amp

clara_mri_fed_learning_seg_brain_tumors_br16_t1c2tc_no_amp

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Description
clara_mri_fed_learning_seg_brain_tumors_br16_t1c2tc_no_amp is a pre-trained model for volumetric (3D) brain tumor segmentation (only TC from T1c images).
Publisher
NVIDIA
Latest Version
1
Modified
April 4, 2023
Size
56.33 MB

Description

clara_mri_fed_learning_seg_brain_tumors_br16_t1c2tc_no_amp is a pre-trained model for volumetric (3D) brain tumor segmentation (only TC from T1c images).

Model Overview

Data

The model is trained to segment "tumor core" (TC) based on 1 input MRI scan (T1c).

The dataset is available at "Multimodal Brain Tumor Segmentation Challenge (BraTS) 2018." The provided labelled data was partitioned, based our own split, into training (243 studies) and validation (42 studies) datasets, as shown in config/seg_brats18_datalist_t1c.json.

For more detailed description of tumor regions, please see the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2018 data page at https://www.med.upenn.edu/sbia/brats2018/data.html.

FL training

This training task was performed with Federated Learning with AMP disabled on all clients and server. Two clients joined the training task. Each client was trained on one of the two disjoint datasets.

Training configuration

This model utilized a similar approach described in 3D MRI brain tumor segmentation using autoencoder regularization, which was a winning method in BraTS2018 [1].

The provided training configuration required 16GB GPU memory.

Model Input Shape: 224 x 224 x 128

Training Script:

  • server_train.sh
  • client1_train.sh
  • client2_train.sh

Input and output formats

Input: single channel 3D MRIs (T1c)

Output: single channels of tumor core 3D masks

Scores

The achieved mean Dice score on the validation data is

  • tumor core: 0.850

Availability

In order to access this model, please apply for general availability access at 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.

Compatibility

This model is only compatible with Clara Train SDK v2.0 and will not work with v1.1 and v1.0.

Disclaimer

The content of this model is only an example. It is not intended to be a substitute for professional medical advice, diagnosis, or treatment.

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.

Reference

[1] Myronenko, Andriy. "3D MRI brain tumor segmentation using autoencoder regularization." International MICCAI Brainlesion Workshop. Springer, Cham, 2018. https://arxiv.org/abs/1810.11654.