A federated learning demo for volumetric (3D) segmentation of brain tumors from multimodal MRIs based on BraTS 2018 data.
Note: The 4.1 version of this model is only compatible with the 4.1 version of the Clara Train SDK container
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:
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 (
"DATASET_JSON" value in
config/environment.json should be changed to
"config/seg_brats18_datalist_client_a.json" for site-1, and
"config/seg_brats18_datalist_client_b.json" for site-2.
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 site-1 (validation 30; testing 36) and 56 Low-grade for site-2 (validation 12; testing 7), with our own split, as shown in the datalist json files in config folder.
A figure of two clients training with 600 federated rounds:
A figure of local site validation of two clients during the 600-round federated learning:
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 site-1 and 12 cases on site-2).
Site-1: TC: 0.8790 WT: 0.9180 ET: 0.8064 Site-2: TC: 0.6223 WT: 0.8841 ET: 0.5908 AVE: TC: 0.8057 WT: 0.9083 ET: 0.7448
Site-1: TC: 0.8812 WT: 0.9152 ET: 0.7877 Site-2: TC: 0.6053 WT: 0.8683 ET: 0.5961 AVE: TC: 0.8024 WT: 0.9018 ET: 0.7329
Site-1: TC: 0.8811 WT: 0.9157 ET: 0.7876 Site-2: TC: 0.6427 WT: 0.8882 ET: 0.5109 AVE: TC: 0.8130 WT: 0.9078 ET: 0.7085
Site-1: TC: 0.8798 WT: 0.9137 ET: 0.7895 Site-2: TC: 0.6161 WT: 0.8676 ET: 0.5009 AVE: TC: 0.8045 WT: 0.9005 ET: 0.7070
The cross-site average (
AVE) is computed from
(Site-1_mean x 30 + Site-2_mean x 12) / (30 + 12).
An example of the cross-site validation results are available at
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.
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