Conditional Segmentation in Lieu of Image Registration

TL;DR

This paper introduces a novel approach to image registration by learning to locate corresponding regions of interest directly through conditional segmentation, simplifying the process and improving accuracy in medical imaging applications.

Contribution

It proposes replacing traditional registration with a conditional segmentation method focused on ROI localization, demonstrating improved accuracy in prostate MRI and ultrasound registration.

Findings

Median TRE of 2.1 mm for prostate ROI registration

Over 34% reduction in TRE compared to previous methods

Quantitative bias-variance analysis explains accuracy improvements

Abstract

Classical pairwise image registration methods search for a spatial transformation that optimises a numerical measure that indicates how well a pair of moving and fixed images are aligned. Current learning-based registration methods have adopted the same paradigm and typically predict, for any new input image pair, dense correspondences in the form of a dense displacement field or parameters of a spatial transformation model. However, in many applications of registration, the spatial transformation itself is only required to propagate points or regions of interest (ROIs). In such cases, detailed pixel- or voxel-level correspondence within or outside of these ROIs often have little clinical value. In this paper, we propose an alternative paradigm in which the location of corresponding image-specific ROIs, defined in one image, within another image is learnt. This results in replacing image registration by a conditional segmentation algorithm, which can build on typical image segmentation networks and their widely-adopted training strategies. Using the registration of 3D MRI and ultrasound images of the prostate as an example to demonstrate this new approach, we report a median target registration error (TRE) of 2.1 mm between the ground-truth ROIs defined on intraoperative ultrasound images and those propagated from the preoperative MR images. Significantly lower (>34%) TREs were obtained using the proposed conditional segmentation compared with those obtained from a previously-proposed spatial-transformation-predicting registration network trained with the same multiple ROI labels for individual image pairs. We conclude this work by using a quantitative bias-variance analysis to provide one explanation of the observed improvement in registration accuracy.