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arXiv · 1907.07807

A fully 3D multi-path convolutional neural network with feature fusion and feature weighting for automatic lesion identification in brain MRI images

Yunzhe Xue·Meiyan Xie·Fadi G. Farhat·Olga Boukrina·A. M. Barrett·Jeffrey R. Binder·Usman W. Roshan·William W. Graves
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Abstract

We propose a fully 3D multi-path convolutional network to predict stroke lesions from 3D brain MRI images. Our multi-path model has independent encoders for different modalities containing residual convolutional blocks, weighted multi-path feature fusion from different modalities, and weighted fusion modules to combine encoder and decoder features. Compared to existing 3D CNNs like DeepMedic, 3D U-Net, and AnatomyNet, our networks achieves the highest statistically significant cross-validation accuracy of 60.5% on the large ATLAS benchmark of 220 patients. We also test our model on multi-modal images from the Kessler Foundation and Medical College Wisconsin and achieve a statistically significant cross-validation accuracy of 65%, significantly outperforming the multi-modal 3D U-Net and DeepMedic. Overall our model offers a principled, extensible multi-path approach that outperforms multi-channel alternatives and achieves high Dice accuracies on existing benchmarks.

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Yunzhe Xue, Meiyan Xie, Fadi G. Farhat, Olga Boukrina, A. M. Barrett, Jeffrey R. Binder, Usman W. Roshan, William W. Graves. 2019-11-16. A fully 3D multi-path convolutional neural network with feature fusion and feature weighting for automatic lesion identification in brain MRI images. https://arxiv.org/abs/1907.07807

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