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Waldmannstetter, D.* ; Wiestler, B.* ; Schwarting, J.* ; Ezhov, I.* ; Metz, M.* ; Bakas, S.* ; Baheti, B.* ; Chakrabarty, S.* ; Rueckert, D.* ; Kirschke, J.S.* ; Heckemann, R.A.* ; Piraud, M. ; Menze, B.H.* ; Kofler, F.

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)
DOI
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
Korrespondenzautor
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, Heft: , Seiten: 57-68 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)