@inproceedings{e5c2e7ada4fc4908a3d6f0d78a7c6d50,
title = "Deformable atlas for multi-structure segmentation",
abstract = "We develop a novel deformable atlas method for multi-structure segmentation that seamlessly combines the advantages of image-based and atlas-based methods. The method formulates a probabilistic framework that combines prior anatomical knowledge with image-based cues that are specific to the subject's anatomy, and solves it using expectation-maximization method. It improves the segmentation over conventional label fusion methods especially around the structure boundaries, and is robust to large anatomical variation. The proposed method was applied to segment multiple structures in both normal and diseased brains and was shown to significantly improve results especially in diseased brains.",
keywords = "deformable atlas, GVF, label fusion, MLE, Segmentation",
author = "Xiaofeng Liu and Albert Montillo and Tan, {Ek T.} and Schenck, {John F.} and Paulo Mendonca",
year = "2013",
month = oct,
day = "23",
doi = "10.1007/978-3-642-40811-3_93",
language = "English (US)",
isbn = "9783642408106",
volume = "8149 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 1",
pages = "743--750",
booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings",
edition = "PART 1",
note = "16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 ; Conference date: 22-09-2013 Through 26-09-2013",
}