Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration

Kleopatra Pirpinia,Jan-Jakob Sonke,Tanja Alderliesten,Marcel Van Herk,Peter A N Bosman

doi:10.3390/a12050099

Abstract

Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.

Highlights

Deformable image registration (DIR) [1], i.e., the process of searching for the optimal non-linear transformation to align two images, plays an increasingly important role in radiotherapy [2], with applications ranging from radiotherapy planning [3], dose accumulation [4], contour propagation [5], to radiotherapy response monitoring [6].One of the challenges of deformable image registration (DIR) in clinical practice concerns the parameter choices that need to be made for each registration instance, since the success of most registration methods depends on setting a variety of parameters well
We developed and tested our evolutionary multi-objective machine learning approach on two breast magnetic resonance (MR) DIR problems of different levels of complexity
TREdi f f was extremely small for all ten cases (Table 1), indicating that the class solution approach was capable of obtaining a solution(s) of high quality, in spite of the poor result for Case 8 from an evolutionary multi-objective machine learning standpoint

Summary

Introduction

Deformable image registration (DIR) [1], i.e., the process of searching for the optimal non-linear transformation to align two images, plays an increasingly important role in radiotherapy [2], with applications ranging from radiotherapy planning [3], dose accumulation [4], contour propagation [5], to radiotherapy response monitoring [6].One of the challenges of DIR in clinical practice concerns the parameter choices that need to be made for each registration instance, since the success of most registration methods depends on setting a variety of parameters well (e.g., the weights in the cost function, the number of registration levels, Algorithms 2019, 12, 99; doi:10.3390/a12050099 www.mdpi.com/journal/algorithmsAlgorithms 2019, 12, 99 the control point grid spacing). The weights of the cost function are especially important, as they determine the trade-off between all the objectives of interest, including the effect of regularization of the DIR problem, which is necessary, as DIR is inherently ill-posed [7]. Optimizing these parameters for each individual DIR instance is challenging in clinical practice. Having a class solution for DIR, i.e., a configuration of parameters for which DIR performs well on all instances of a DIR problem, would facilitate wide-scale clinical application. Several approaches have been proposed [8,9,10], still, often, parameters are manually tuned for each case of the DIR problem separately via trial-and-error adaptations, followed by visual inspection of the registration outcome

Methods

Results

Discussion

Conclusion