Domain adaptation for segmentation of critical structures for prostate cancer therapy

Abstract Preoperative assessment of the proximity of critical structures to the tumors is crucial in avoiding unnecessary damage during prostate cancer treatment. A patient-specific 3D anatomical model of those structures, namely the neurovascular bundles (NVB) and the external urethral sphincters (...

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Autores principales:	Anneke Meyer, Alireza Mehrtash, Marko Rak, Oleksii Bashkanov, Bjoern Langbein, Alireza Ziaei, Adam S. Kibel, Clare M. Tempany, Christian Hansen, Junichi Tokuda
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0b307645ba7c4525a5208fe3cfd85cda
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spelling	oai:doaj.org-article:0b307645ba7c4525a5208fe3cfd85cda2021-12-02T18:25:05ZDomain adaptation for segmentation of critical structures for prostate cancer therapy10.1038/s41598-021-90294-42045-2322https://doaj.org/article/0b307645ba7c4525a5208fe3cfd85cda2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90294-4https://doaj.org/toc/2045-2322Abstract Preoperative assessment of the proximity of critical structures to the tumors is crucial in avoiding unnecessary damage during prostate cancer treatment. A patient-specific 3D anatomical model of those structures, namely the neurovascular bundles (NVB) and the external urethral sphincters (EUS), can enable physicians to perform such assessments intuitively. As a crucial step to generate a patient-specific anatomical model from preoperative MRI in a clinical routine, we propose a multi-class automatic segmentation based on an anisotropic convolutional network. Our specific challenge is to train the network model on a unique source dataset only available at a single clinical site and deploy it to another target site without sharing the original images or labels. As network models trained on data from a single source suffer from quality loss due to the domain shift, we propose a semi-supervised domain adaptation (DA) method to refine the model’s performance in the target domain. Our DA method combines transfer learning and uncertainty guided self-learning based on deep ensembles. Experiments on the segmentation of the prostate, NVB, and EUS, show significant performance gain with the combination of those techniques compared to pure TL and the combination of TL with simple self-learning ( $${p}<0.005$$ p < 0.005 for all structures using a Wilcoxon’s signed-rank test). Results on a different task and data (Pancreas CT segmentation) demonstrate our method’s generic application capabilities. Our method has the advantage that it does not require any further data from the source domain, unlike the majority of recent domain adaptation strategies. This makes our method suitable for clinical applications, where the sharing of patient data is restricted.Anneke MeyerAlireza MehrtashMarko RakOleksii BashkanovBjoern LangbeinAlireza ZiaeiAdam S. KibelClare M. TempanyChristian HansenJunichi TokudaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Anneke Meyer Alireza Mehrtash Marko Rak Oleksii Bashkanov Bjoern Langbein Alireza Ziaei Adam S. Kibel Clare M. Tempany Christian Hansen Junichi Tokuda Domain adaptation for segmentation of critical structures for prostate cancer therapy
description	Abstract Preoperative assessment of the proximity of critical structures to the tumors is crucial in avoiding unnecessary damage during prostate cancer treatment. A patient-specific 3D anatomical model of those structures, namely the neurovascular bundles (NVB) and the external urethral sphincters (EUS), can enable physicians to perform such assessments intuitively. As a crucial step to generate a patient-specific anatomical model from preoperative MRI in a clinical routine, we propose a multi-class automatic segmentation based on an anisotropic convolutional network. Our specific challenge is to train the network model on a unique source dataset only available at a single clinical site and deploy it to another target site without sharing the original images or labels. As network models trained on data from a single source suffer from quality loss due to the domain shift, we propose a semi-supervised domain adaptation (DA) method to refine the model’s performance in the target domain. Our DA method combines transfer learning and uncertainty guided self-learning based on deep ensembles. Experiments on the segmentation of the prostate, NVB, and EUS, show significant performance gain with the combination of those techniques compared to pure TL and the combination of TL with simple self-learning ( $${p}<0.005$$ p < 0.005 for all structures using a Wilcoxon’s signed-rank test). Results on a different task and data (Pancreas CT segmentation) demonstrate our method’s generic application capabilities. Our method has the advantage that it does not require any further data from the source domain, unlike the majority of recent domain adaptation strategies. This makes our method suitable for clinical applications, where the sharing of patient data is restricted.
format	article
author	Anneke Meyer Alireza Mehrtash Marko Rak Oleksii Bashkanov Bjoern Langbein Alireza Ziaei Adam S. Kibel Clare M. Tempany Christian Hansen Junichi Tokuda
author_facet	Anneke Meyer Alireza Mehrtash Marko Rak Oleksii Bashkanov Bjoern Langbein Alireza Ziaei Adam S. Kibel Clare M. Tempany Christian Hansen Junichi Tokuda
author_sort	Anneke Meyer
title	Domain adaptation for segmentation of critical structures for prostate cancer therapy
title_short	Domain adaptation for segmentation of critical structures for prostate cancer therapy
title_full	Domain adaptation for segmentation of critical structures for prostate cancer therapy
title_fullStr	Domain adaptation for segmentation of critical structures for prostate cancer therapy
title_full_unstemmed	Domain adaptation for segmentation of critical structures for prostate cancer therapy
title_sort	domain adaptation for segmentation of critical structures for prostate cancer therapy
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0b307645ba7c4525a5208fe3cfd85cda
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Domain adaptation for segmentation of critical structures for prostate cancer therapy

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