@inproceedings{d02ef84ddd5d4fc684abe9df6aa9a6b4,
title = "Geodesic patch-based segmentation",
abstract = "Label propagation has been shown to be effective in many automatic segmentation applications. However, its reliance on accurate image alignment means that segmentation results can be affected by any registration errors which occur. Patch-based methods relax this dependence by avoiding explicit one-to-one correspondence assumptions between images but are still limited by the search window size. Too small, and it does not account for enough registration error; too big, and it becomes more likely to select incorrect patches of similar appearance for label fusion. This paper presents a novel patch-based label propagation approach which uses relative geodesic distances to define patient-specific coordinate systems as spatial context to overcome this problem. The approach is evaluated on multi-organ segmentation of 20 cardiac MR images and 100 abdominal CT images, demonstrating competitive results.",
author = "Zehan Wang and Bhatia, \{Kanwal K.\} and Ben Glocker and Antonio Marvao and Tim Dawes and Kazunari Misawa and Kensaku Mori and Daniel Rueckert",
year = "2014",
doi = "10.1007/978-3-319-10404-1\_83",
language = "English",
isbn = "9783319104034",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 1",
pages = "666--673",
booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings",
edition = "PART 1",
note = "17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 ; Conference date: 14-09-2014 Through 18-09-2014",
}