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Open AccessArticle

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

1
Department of Radiation Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
2
Life Sciences and Health Group, Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands
3
Manchester Cancer Research Centre, Division of Cancer Science, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Sciences Centre, Manchester M13 9PL, UK
4
Department of Radiation Oncology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(5), 99; https://doi.org/10.3390/a12050099
Received: 30 March 2019 / Revised: 1 May 2019 / Accepted: 7 May 2019 / Published: 9 May 2019
(This article belongs to the Special Issue Evolutionary Algorithms in Health Technologies)
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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. View Full-Text
Keywords: evolutionary machine learning; multi-objective optimization; evolutionary algorithms; class solutions; deformable image registration; breast cancer evolutionary machine learning; multi-objective optimization; evolutionary algorithms; class solutions; deformable image registration; breast cancer
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Pirpinia, K.; Bosman, P.A.N.; Sonke, J.-J.; van Herk, M.; Alderliesten, T. Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration. Algorithms 2019, 12, 99.

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