Segmentation-guided Medical Image Registration

Abstract

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.