Small organ segmentation in whole-body mri using a two-stage fcn and weighting schemes
Valindria VV, Lavdas I, Cerrolaza J, Aboagye EO, Rockall AG, Rueckert D, Glocker B (2018)
Publication Type: Conference contribution
Publication year: 2018
Journal
Publisher: Springer Verlag
Book Volume: 11046 LNCS
Pages Range: 346-354
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Granada, ESP
ISBN: 9783030009182
DOI: 10.1007/978-3-030-00919-9_40
Abstract
Accurate and robust segmentation of small organs in whole-body MRI is difficult due to anatomical variation and class imbalance. Recent deep network based approaches have demonstrated promising performance on abdominal multi-organ segmentations. However, the performance on small organs is still suboptimal as these occupy only small regions of the whole-body volumes with unclear boundaries and variable shapes. A coarse-to-fine, hierarchical strategy is a common approach to alleviate this problem, however, this might miss useful contextual information. We propose a two-stage approach with weighting schemes based on auto-context and spatial atlas priors. Our experiments show that the proposed approach can boost the segmentation accuracy of multiple small organs in whole-body MRI scans.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Valindria, V.V., Lavdas, I., Cerrolaza, J., Aboagye, E.O., Rockall, A.G., Rueckert, D., & Glocker, B. (2018). Small organ segmentation in whole-body mri using a two-stage fcn and weighting schemes. In Mingxia Liu, Heung-Il Suk, Yinghuan Shi (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 346-354). Granada, ESP: Springer Verlag.
MLA:
Valindria, Vanya V., et al. "Small organ segmentation in whole-body mri using a two-stage fcn and weighting schemes." Proceedings of the 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Mingxia Liu, Heung-Il Suk, Yinghuan Shi, Springer Verlag, 2018. 346-354.
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