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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Primitive simultaneous optimization of similarity metrics for image registration.
In: (Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries). Berlin [u.a.]: Springer, 2024. 57-68 (Lect. Notes Comput. Sc. ; 14668 LNCS)
Even though simultaneous optimization of similarity metrics is a standard procedure in the field of semantic segmentation, surprisingly, this is much less established for image registration. To help closing this gap in the literature, we investigate in a complex multi-modal 3D setting whether simultaneous optimization of registration metrics, here implemented by means of primitive summation, can benefit image registration. We evaluate two challenging datasets containing collections of pre- to post-operative and pre- to intra-operative Magnetic Resonance (MR) images of glioma. Employing the proposed optimization, we demonstrate improved registration accuracy in terms of Target Registration Error (TRE) o on expert neuroradiologists’ landmark annotations.
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Publikationstyp
Artikel: Konferenzbeitrag
Schlagwörter
Brain Tumor ; Glioma ; Loss Function ; Registration ; Similarity Metric
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Konferenztitel
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Zeitschrift
Lecture Notes in Computer Science
Quellenangaben
Band: 14668 LNCS,
Seiten: 57-68
Verlag
Springer
Verlagsort
Berlin [u.a.]
Förderungen
Helmut Horten Foundation
Deutsche Forschungsgemeinschaft (DFG) through TUM International Graduate School of Science and Engineering (IGSSE)
Deutsche Forschungsgemeinschaft (DFG) through TUM International Graduate School of Science and Engineering (IGSSE)