Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical
Segmentation
Abstract
Deep learning models such as convolutional neural network 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 multimodalities, 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 [13] show that our method outperforms state-of-the-art biomedical segmentation approaches