A federated learning demo for volumetric (3D) segmentation of brain tumors from multimodal MRIs based on BraTS 2018 data.
Two clients share the global training models via a federated learning server for every epoch of local training. The overall illustration of the federated training is shown below:
For more information, please see our Clara Train Federated Learning example notebooks.
The local model for each federated client utilized a similar approach described in 3D MRI brain tumor segmentation using autoencoder regularization, which was a winning method in BraTS2018 . The client's training was performed with the following:
The training was performed with the following:
The model is trained to segment 3 nested subregions of primary (gliomas) brain tumors: the "enhancing tumor" (ET), the "tumor core" (TC), the "whole tumor" (WT) based on 4 input MRI scans (T1c, T1, T2, FLAIR). The ET is described by areas that show hyperintensity in T1c when compared to T1, but also when compared to "healthy" white matter in T1c. The TC describes the bulk of the tumor, which is what is typically resected. The TC entails the ET, as well as the necrotic (fluid-filled) and the non-enhancing (solid) parts of the tumor. The WT describes the complete extent of the disease, as it entails the TC and the peritumoral edema (ED), which is typically depicted by hyperintense signal in FLAIR.
The dataset is available at "Multimodal Brain Tumor Segmentation Challenge (BraTS) 2018." The provided labelled data was partitioned, based our own split, into training (200 studies), validation (42 studies) and testing (43 studies) datasets.
For more detailed descriptions 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.
When deploying the MMAR to the clients, the
"DATASET_JSON" value in
config/environment.json should be changed to
"config/seg_brats18_datalist_client_a.json" for client A, and
"config/seg_brats18_datalist_client_b.json" for client B.
Similarly, when evaluating the models, the
commands/validate.sh is by default configured to run the MMAR model with
"config/seg_brats18_datalist_client_a.json" testing data. The
"DATASET_JSON" should be adjusted to the target
data list if needed.
The model was trained with 144 High-grade for client A (validation 30; testing 36) and 56 Low-grade for client B (validation 12; testing 7), with our own split, as shown in the datalist json files in config folder.
The following report uses TC to denote "Tumor Core", WT to denote "Whole Tumor", ET to denote "Enhancing Tumor". The results are based on the testing sets (30 cases on Client A and 12 cases on client B).
Data-A: TC: 0.8707 WT: 0.9146 ET: 0.7931 Data-B: TC: 0.5929 WT: 0.8793 ET: 0.5617 AVE: TC: 0.7913 WT: 0.9045 ET: 0.7270
Data-A: TC: 0.8655 WT: 0.9157 ET: 0.7797 Data-B: TC: 0.5604 WT: 0.8514 ET: 0.5356 AVE: TC: 0.7783 WT: 0.8973 ET: 0.7100
Data-A: TC: 0.8781 WT: 0.9170 ET: 0.7921 Data-B: TC: 0.5360 WT: 0.8469 ET: 0.4472 AVE: TC: 0.7804 WT: 0.8970 ET: 0.6936
The cross-site average (
AVE) is computed from
(client_A_mean x 30 + client_B_mean x 12) / (30 + 12)
where client A has 30 test cases, client B has 12.
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: 4 channel MRI (4 aligned MRIs T1c, T1, T2, FLAIR at 1x1x1 mm)
Augmentation in training:
Output: 3 channels
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, Andriy. "3D MRI brain tumor segmentation using autoencoder regularization." International MICCAI Brainlesion Workshop. Springer, Cham, 2018. https://arxiv.org/abs/1810.11654.
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.