{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:15:46Z","timestamp":1760188546361,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T00:00:00Z","timestamp":1557360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004622","name":"KWF Kankerbestrijding","doi-asserted-by":"publisher","award":["KWF 2012-5716"],"award-info":[{"award-number":["KWF 2012-5716"]}],"id":[{"id":"10.13039\/501100004622","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/a12050099","type":"journal-article","created":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T11:22:35Z","timestamp":1557400955000},"page":"99","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration"],"prefix":"10.3390","volume":"12","author":[{"given":"Kleopatra","family":"Pirpinia","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter A. N.","family":"Bosman","sequence":"additional","affiliation":[{"name":"Life Sciences and Health Group, Centrum Wiskunde &amp; Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan-Jakob","family":"Sonke","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcel","family":"van Herk","sequence":"additional","affiliation":[{"name":"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"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tanja","family":"Alderliesten","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TMI.2013.2265603","article-title":"Deformable medical image registration: A survey","volume":"32","author":"Sotiras","year":"2013","journal-title":"IEEE Trans. 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