# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1907.00438