Multi-agent shape models for hip landmark detection in MR scans
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Landmark detection is an essential step in the diagnosis of bone pathologies and pelvis morphometry. Hence, we propose a Deep Learning based method for automatic landmark detection on multi-modality hips magnetic resonance (MR) scans. Our method is based on a synergistic analysis of appearance and shape information by using deep networks for the detection of landmark candidate locations and then adjusting these locations using inter-landmark spatial properties. Our best model gives an average of 1.74 mm over all the landmarks, where 67% of the proposed landmarks are within the spatial matching error of at most 2mm.
Original language | English |
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Title of host publication | Medical Imaging 2021 : Image Processing |
Editors | Ivana Isgum, Bennett A. Landman |
Number of pages | 11 |
Publisher | SPIE - International Society for Optical Engineering |
Publication date | 2021 |
Article number | 115960O |
ISBN (Electronic) | 9781510640214 |
DOIs | |
Publication status | Published - 2021 |
Event | SPIE Medical Imaging 2021 - Virtual, Online, United States Duration: 15 Feb 2021 → 19 Feb 2021 |
Conference
Conference | SPIE Medical Imaging 2021 |
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Land | United States |
By | Virtual, Online |
Periode | 15/02/2021 → 19/02/2021 |
Sponsor | The Society of Photo-Optical Instrumentation Engineers (SPIE) |
Series | Proceedings of S P I E - International Society for Optical Engineering |
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Volume | 11596 |
ISSN | 0277-786X |
Bibliographical note
Publisher Copyright:
© 2021 SPIE.
- Deep Learning, Landmark Detection, Medical image processing
Research areas
ID: 283138277