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We propose a fully 3D multi-path convolutional network to predict strokelesions from 3D brain MRI images. Our multi-path model has independent encodersfor different modalities containing residual convolutional blocks, weightedmulti-path feature fusion from different modalities, and weighted fusionmodules to combine encoder and decoder features. Compared to existing 3D CNNslike DeepMedic, 3D U-Net, and AnatomyNet, our networks achieves the higheststatistically significant cross-validation accuracy of 60.5% on the large ATLASbenchmark of 220 patients. We also test our model on multi-modal images fromthe Kessler Foundation and Medical College Wisconsin and achieve astatistically significant cross-validation accuracy of 65%, significantlyoutperforming the multi-modal 3D U-Net and DeepMedic. Overall our model offersa principled, extensible multi-path approach that outperforms multi-channelalternatives and achieves high Dice accuracies on existing benchmarks.

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