@inproceedings{21e38a4bbba34d3ea11225756f79f6a9,
title = "LSTM-MA: A LSTM Method with Multi-Modality and Adjacency Constraint for Brain Image Segmentation",
abstract = "MR brain tissue segmentation is a significant problem in biomedical image processing. Inhomogeneous intensity and image noise influence the segmentation accuracy. In this paper, we propose a LSTM method with multi-modality and adjacency constraint for brain image segmentation, named LSTM-MA. Two feature sequence generation ways in our method are used, i.e., features with pixel-wise and superpixel-wise adjacency constraint. The LSTM model classifies the generated features into semantic labels to form the segmentation result. The evaluation experiments on BrainWeb and MRBrainS demonstrate that the proposed LSTM-MA with pixel-wise adjacency constraint achieves promising segmentation results, while LSTM-MA with superpixel-wise adjacency constraint shows its computational efficiency as well as robustness to noise.",
keywords = "Brain Segmentation, LSTM, Multi-modality, Noise Robustness, Superpixel",
author = "Kai Xie and Ying Wen",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 26th IEEE International Conference on Image Processing, ICIP 2019 ; Conference date: 22-09-2019 Through 25-09-2019",
year = "2019",
month = sep,
doi = "10.1109/ICIP.2019.8802959",
language = "英语",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "240--244",
booktitle = "2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings",
address = "美国",
}