Deep learning models such as convolutional neural net- work have been widely used in 3D biomedical segmentation and achieve state-of-the-art performance. However, most of them often adapt a single modality or stack multiple modalities as different input channels. To better leverage the multi- modalities, we propose a deep encoder-decoder structure with cross-modality convolution layers to incorporate different modalities of MRI data. In addition, we exploit convolutional LSTM to model a sequence of 2D slices, and jointly learn the multi-modalities and convolutional LSTM in an end-to-end manner. To avoid converging to the certain labels, we adopt a re-weighting scheme and two-phase training to handle the label imbalance. Experimental results on BRATS-2015 show that our method outperforms state-of-the-art biomedical segmentation approaches.
@article{arxiv.1704.07754,
title = {Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation},
author = {Kuan-Lun Tseng and Yen-Liang Lin and Winston Hsu and Chung-Yang Huang},
journal= {arXiv preprint arXiv:1704.07754},
year = {2017}
}