Multi-agent shape models for hip landmark detection in MR scans
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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.
Originalsprog | Engelsk |
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Titel | Medical Imaging 2021 : Image Processing |
Redaktører | Ivana Isgum, Bennett A. Landman |
Antal sider | 11 |
Forlag | SPIE - International Society for Optical Engineering |
Publikationsdato | 2021 |
Artikelnummer | 115960O |
ISBN (Elektronisk) | 9781510640214 |
DOI | |
Status | Udgivet - 2021 |
Begivenhed | SPIE Medical Imaging 2021 - Virtual, Online, USA Varighed: 15 feb. 2021 → 19 feb. 2021 |
Konference
Konference | SPIE Medical Imaging 2021 |
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Land | USA |
By | Virtual, Online |
Periode | 15/02/2021 → 19/02/2021 |
Sponsor | The Society of Photo-Optical Instrumentation Engineers (SPIE) |
Navn | Proceedings of S P I E - International Society for Optical Engineering |
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Vol/bind | 11596 |
ISSN | 0277-786X |
ID: 283138277