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Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones
Automatic segmentation of the prostate of and the prostatic zones on MRI remains one of the most compelling research areas. While different image enhancement techniques are emerging as powerful tools for improving the performance of segmentation algorithms, their application still lacks consensus du...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9837765/ https://www.ncbi.nlm.nih.gov/pubmed/36639671 http://dx.doi.org/10.1038/s41598-023-27671-8 |
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author | Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos Pezoulas, Vasileios C. Grigoriadis, Grigorios Tsiknakis, Nikos Marias, Kostas Tsiknakis, Manolis Fotiadis, Dimitrios I. |
author_facet | Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos Pezoulas, Vasileios C. Grigoriadis, Grigorios Tsiknakis, Nikos Marias, Kostas Tsiknakis, Manolis Fotiadis, Dimitrios I. |
author_sort | Zaridis, Dimitrios I. |
collection | PubMed |
description | Automatic segmentation of the prostate of and the prostatic zones on MRI remains one of the most compelling research areas. While different image enhancement techniques are emerging as powerful tools for improving the performance of segmentation algorithms, their application still lacks consensus due to contrasting evidence regarding performance improvement and cross-model stability, further hampered by the inability to explain models’ predictions. Particularly, for prostate segmentation, the effectiveness of image enhancement on different Convolutional Neural Networks (CNN) remains largely unexplored. The present work introduces a novel image enhancement method, named RACLAHE, to enhance the performance of CNN models for segmenting the prostate’s gland and the prostatic zones. The improvement in performance and consistency across five CNN models (U-Net, U-Net++, U-Net3+, ResU-net and USE-NET) is compared against four popular image enhancement methods. Additionally, a methodology is proposed to explain, both quantitatively and qualitatively, the relation between saliency maps and ground truth probability maps. Overall, RACLAHE was the most consistent image enhancement algorithm in terms of performance improvement across CNN models with the mean increase in Dice Score ranging from 3 to 9% for the different prostatic regions, while achieving minimal inter-model variability. The integration of a feature driven methodology to explain the predictions after applying image enhancement methods, enables the development of a concrete, trustworthy automated pipeline for prostate segmentation on MR images. |
format | Online Article Text |
id | pubmed-9837765 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98377652023-01-15 Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos Pezoulas, Vasileios C. Grigoriadis, Grigorios Tsiknakis, Nikos Marias, Kostas Tsiknakis, Manolis Fotiadis, Dimitrios I. Sci Rep Article Automatic segmentation of the prostate of and the prostatic zones on MRI remains one of the most compelling research areas. While different image enhancement techniques are emerging as powerful tools for improving the performance of segmentation algorithms, their application still lacks consensus due to contrasting evidence regarding performance improvement and cross-model stability, further hampered by the inability to explain models’ predictions. Particularly, for prostate segmentation, the effectiveness of image enhancement on different Convolutional Neural Networks (CNN) remains largely unexplored. The present work introduces a novel image enhancement method, named RACLAHE, to enhance the performance of CNN models for segmenting the prostate’s gland and the prostatic zones. The improvement in performance and consistency across five CNN models (U-Net, U-Net++, U-Net3+, ResU-net and USE-NET) is compared against four popular image enhancement methods. Additionally, a methodology is proposed to explain, both quantitatively and qualitatively, the relation between saliency maps and ground truth probability maps. Overall, RACLAHE was the most consistent image enhancement algorithm in terms of performance improvement across CNN models with the mean increase in Dice Score ranging from 3 to 9% for the different prostatic regions, while achieving minimal inter-model variability. The integration of a feature driven methodology to explain the predictions after applying image enhancement methods, enables the development of a concrete, trustworthy automated pipeline for prostate segmentation on MR images. Nature Publishing Group UK 2023-01-13 /pmc/articles/PMC9837765/ /pubmed/36639671 http://dx.doi.org/10.1038/s41598-023-27671-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos Pezoulas, Vasileios C. Grigoriadis, Grigorios Tsiknakis, Nikos Marias, Kostas Tsiknakis, Manolis Fotiadis, Dimitrios I. Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones |
title | Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones |
title_full | Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones |
title_fullStr | Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones |
title_full_unstemmed | Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones |
title_short | Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones |
title_sort | region-adaptive magnetic resonance image enhancement for improving cnn-based segmentation of the prostate and prostatic zones |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9837765/ https://www.ncbi.nlm.nih.gov/pubmed/36639671 http://dx.doi.org/10.1038/s41598-023-27671-8 |
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