HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation

Jose Dolz, Karthik Gopinath, Jing Yuan, Herve Lombaert, Christian Desrosiers, Ismail Ben Ayed

Keywords: Deep learning, brain MRI, segmentation, 3D CNN, multi-modal imaging

Abstract: In this work we present HyperDenseNet, a recently published 3D fully convolutional neural network that extends the definition of dense connectivity to multi-modal segmentation problems. In this architecture each modality is processed by a separate path, and dense connections occur not only between the pairs of layers within the same path, but also between those across different paths. Hence, the proposed network has total freedom to learn more complex combinations between the different modalities, within and in-between all the levels of abstraction, increasing significantly the learning representation. HyperDenseNet is evaluated on two challenging multi-modal brain tissue segmentation datasets, iSEG 2017 and MRBrainS'13, yielding significant improvements over many state-of-the-art segmentation networks on both benchmarks. Our code is publicly available at https://www.github.com/josedolz/HyperDenseNet.

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Link: https://ieeexplore.ieee.org/document/8515234

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/arxiv:1804.02967/code)