Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation

• Main Authors: Kuan-Lun Tseng, 曾冠綸
• Other Authors: Chung-Yang (Ric) Huang
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/rkmn36

碩士 === 國立臺灣大學 === 電機工程學研究所 === 106 === 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 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 [13] show that our method outperforms state-of-the- art biomedical segmentation approaches.