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      Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology

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          Abstract

          Background

          Accurate prostate zonal segmentation on magnetic resonance images (MRI) is a critical prerequisite for automated prostate cancer detection. We aimed to assess the variability of manual prostate zonal segmentation by radiologists on T2-weighted (T2W) images, and to study factors that may influence it.

          Methods

          Seven radiologists of varying levels of experience segmented the whole prostate gland (WG) and the transition zone (TZ) on 40 axial T2W prostate MRI images (3D T2W images for all patients, and both 3D and 2D images for a subgroup of 12 patients). Segmentation variabilities were evaluated based on: anatomical and morphological variation of the prostate (volume, retro-urethral lobe, intensity contrast between zones, presence of a PI-RADS ≥ 3 lesion), variation in image acquisition (3D vs 2D T2W images), and reader’s experience. Several metrics including Dice Score (DSC) and Hausdorff Distance were used to evaluate differences, with both a pairwise and a consensus (STAPLE reference) comparison.

          Results

          DSC was 0.92 (± 0.02) and 0.94 (± 0.03) for WG, 0.88 (± 0.05) and 0.91 (± 0.05) for TZ respectively with pairwise comparison and consensus reference. Variability was significantly ( p < 0.05) lower for the mid-gland (DSC 0.95 (± 0.02)), higher for the apex (0.90 (± 0.06)) and the base (0.87 (± 0.06)), and higher for smaller prostates ( p < 0.001) and when contrast between zones was low ( p < 0.05). Impact of the other studied factors was non-significant.

          Conclusions

          Variability is higher in the extreme parts of the gland, is influenced by changes in prostate morphology (volume, zone intensity ratio), and is relatively unaffected by the radiologist’s level of expertise.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13244-021-01010-9.

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          Most cited references24

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          V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

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            Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2

            The Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) was developed with a consensus-based process using a combination of published data, and expert observations and opinions. In the short time since its release, numerous studies have validated the value of PI-RADS v2 but, as expected, have also identified a number of ambiguities and limitations, some of which have been documented in the literature with potential solutions offered. To address these issues, the PI-RADS Steering Committee, again using a consensus-based process, has recommended several modifications to PI-RADS v2, maintaining the framework of assigning scores to individual sequences and using these scores to derive an overall assessment category. This updated version, described in this article, is termed PI-RADS v2.1. It is anticipated that the adoption of these PI-RADS v2.1 modifications will improve inter-reader variability and simplify PI-RADS assessment of prostate magnetic resonance imaging even further. Research on the value and limitations on all components of PI-RADS v2.1 is strongly encouraged.
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              EAU-ESTRO-SIOG Guidelines on Prostate Cancer. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent.

              To present a summary of the 2016 version of the European Association of Urology (EAU) - European Society for Radiotherapy & Oncology (ESTRO) - International Society of Geriatric Oncology (SIOG) Guidelines on screening, diagnosis, and local treatment with curative intent of clinically localised prostate cancer (PCa).

                Author and article information

                Contributors
                sarah.montagne@aphp.fr
                Journal
                Insights Imaging
                Insights Imaging
                Insights into Imaging
                Springer International Publishing (Cham )
                1869-4101
                5 June 2021
                5 June 2021
                December 2021
                : 12
                : 71
                Affiliations
                [1 ]GRID grid.50550.35, ISNI 0000 0001 2175 4109, Academic Department of Radiology, Hôpital Pitié-Salpétrière, , Assistance Publique des Hôpitaux de Paris, ; Paris, France
                [2 ]GRID grid.50550.35, ISNI 0000 0001 2175 4109, Academic Department of Radiology, Hôpital Tenon, , Assistance Publique des Hôpitaux de Paris, ; Paris, France
                [3 ]GRID grid.460782.f, ISNI 0000 0004 4910 6551, Inria, Epione Team, , Université Côte D’Azur, ; Sophia Antipolis, Nice, France
                [4 ]GRID grid.462844.8, ISNI 0000 0001 2308 1657, Sorbonne Universités, ; GRC n° 5, Oncotype-Uro, Paris, France
                Author information
                http://orcid.org/0000-0003-2571-3077
                Article
                1010
                10.1186/s13244-021-01010-9
                8179870
                34089410
                1eeb084f-c072-48a2-80fc-a7054286fbf1
                © The Author(s) 2021

                Open AccessThis 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/.

                History
                : 18 January 2021
                : 5 May 2021
                Categories
                Original Article
                Custom metadata
                © The Author(s) 2021

                Radiology & Imaging
                prostate,mri,segmentation,zones,atlas
                Radiology & Imaging
                prostate, mri, segmentation, zones, atlas

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