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Raveendran, V.* ; Spieker, V. ; Braren, R.F.* ; Karampinos, D.C.* ; Zimmer, V.A.* ; Schnabel, J.A.*

Segmentation-guided Medical Image Registration: Quality Awareness using Label Noise Correctionn.

In: Bildverarbeitung für die Medizin 2024. Switzerland: 2024. 33-38 (Inf. aktuell)
DOI
Medical image registration methods can strongly benefit from anatomical labels, which can be provided by segmentation networks at reduced labeling effort. Yet, label noise may adversely affect registration performance. In this work, we propose a quality-aware segmentation-guided registration method that handles such noisy, i.e., low-quality, labels by self-correcting them using Confident Learning. Utilizing NLST and in-house acquired abdominal MR images, we show that our proposed quality-aware method effectively addresses the drop in registration performance observed in quality-unaware methods. Our findings demonstrate that incorporating an appropriate label-correction strategy during training can reduce labeling efforts, consequently enhancing the practicality of segmentation-guided registration.
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Publication type Article: Edited volume or book chapter
Corresponding Author
ISSN (print) / ISBN 1431-472X
e-ISSN 1431-472X
Book Volume Title Bildverarbeitung für die Medizin 2024
Journal Informatik aktuell
Quellenangaben Volume: , Issue: , Pages: 33-38 Article Number: , Supplement: ,
Publishing Place Switzerland
Non-patent literature Publications
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)