Patch-based segmentation without registration: Application to knee MRI
Wang Z, Donoghue C, Rueckert D (2013)
Publication Type: Conference contribution
Publication year: 2013
Journal
Publisher: Springer Verlag
Book Volume: 8184 LNCS
Pages Range: 98-105
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: JPN
ISBN: 9783319022666
DOI: 10.1007/978-3-319-02267-3_13
Abstract
Atlas based segmentation techniques have been proven to be effective in many automatic segmentation applications. However, the reliance on image correspondence means that the segmentation results can be affected by any registration errors which occur, particularly if there is a high degree of anatomical variability. This paper presents a novel multi-resolution patch-based segmentation framework which is able to work on images without requiring registration. Additionally, an image similarity metric using 3D histograms of oriented gradients is proposed to enable atlas selection in this context. We applied the proposed approach to segment MR images of the knee from the MICCAI SKI10 Grand Challenge, where 100 training atlases are provided and evaluation is conducted on 50 unseen test images. The proposed method achieved good scores overall and is comparable to the top entries in the challenge for cartilage segmentation, demonstrating good performance when comparing against state-of-the-art approaches customised to Knee MRI. © 2013 Springer International Publishing.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Wang, Z., Donoghue, C., & Rueckert, D. (2013). Patch-based segmentation without registration: Application to knee MRI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 98-105). JPN: Springer Verlag.
MLA:
Wang, Zehan, Claire Donoghue, and Daniel Rueckert. "Patch-based segmentation without registration: Application to knee MRI." Proceedings of the 4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, JPN Springer Verlag, 2013. 98-105.
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