Startseite
English
Erweiterte Suche
Durchblättern nach ...
Publikationen im Überblick
Ansprechpartner
Hilfe
Postprint
DOI
 |
Open Access Green |
möglich sobald bei der ZB eingereicht worden ist.
Weakly-supervised biomechanically-constrained CT/MRI registration of the spine.
Lect. Notes Comput. Sc. 13436 LNCS, 227-236 (2022)
Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are two of the most informative modalities in spinal diagnostics and treatment planning. CT is useful when analysing bony structures, while MRI gives information about the soft tissue. Thus, fusing the information of both modalities can be very beneficial. Registration is the first step for this fusion. While the soft tissues around the vertebra are deformable, each vertebral body is constrained to move rigidly. We propose a weakly-supervised deep learning framework that preserves the rigidity and the volume of each vertebra while maximizing the accuracy of the registration. To achieve this goal, we introduce anatomy-aware losses for training the network. We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI. We evaluate our method on an in-house dataset of 167 patients. Our results show that adding the anatomy-aware losses increases the plausibility of the inferred transformation while keeping the accuracy untouched.
Altmetric
Weitere Metriken?
Zusatzinfos bearbeiten
[➜Einloggen]
Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Biomechanical Constraints ; Ct/mri Registration ; Deep Learning Image Registration ; Spine
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Konferenztitel
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
Zeitschrift
Lecture Notes in Computer Science
Quellenangaben
Band: 13436 LNCS,
Seiten: 227-236
Verlag
Springer
Verlagsort
Berlin [u.a.]
Nichtpatentliteratur
Publikationen