Literature DB >> 31707168

Variability of manual segmentation of the prostate in axial T2-weighted MRI: A multi-reader study.

Anton S Becker1, Krishna Chaitanya2, Khoschy Schawkat3, Urs J Muehlematter4, Andreas M Hötker4, Ender Konukoglu2, Olivio F Donati4.   

Abstract

PURPOSE: To evaluate the interreader variability in prostate and seminal vesicle (SV) segmentation on T2w MRI.
METHODS: Six readers segmented the peripheral zone (PZ), transitional zone (TZ) and SV slice-wise on axial T2w prostate MRI examinations of n = 80 patients. Twenty different similarity scores, including dice score (DS), Hausdorff distance (HD) and volumetric similarity coefficient (VS), were computed with the VISCERAL EvaluateSegmentation software for all structures combined and separately for the whole gland (WG = PZ + TZ), TZ and SV. Differences between base, midgland and apex were evaluated with DS slice-wise. Descriptive statistics for similarity scores were computed. Wilcoxon testing to evaluate differences of DS, HD and VS was performed.
RESULTS: Overall segmentation variability was good with a mean DS of 0.859 (±SD = 0.0542), HD of 36.6 (±34.9 voxels) and VS of 0.926 (±0.065). The WG showed a DS, HD and VS of 0.738 (±0.144), 36.2 (±35.6 vx) and 0.853 (±0.143), respectively. The TZ showed generally lower variability with a DS of 0.738 (±0.144), HD of 24.8 (±16 vx) and VS of 0.908 (±0.126). The lowest variability was found for the SV with DS of 0.884 (±0.0407), HD of 17 (±10.9 vx) and VS of 0.936 (±0.0509). We found a markedly lower DS of the segmentations in the apex (0.85 ± 0.12) compared to the base (0.87 ± 0.10, p < 0.01) and the midgland (0.89 ± 0.10, p < 0.001).
CONCLUSIONS: We report baseline values for interreader variability of prostate and SV segmentation on T2w MRI. Variability was highest in the apex, lower in the base, and lowest in the midgland.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Interreader agreement; Prostate; Segmentation; Variability; Zonal anatomy

Mesh:

Year:  2019        PMID: 31707168     DOI: 10.1016/j.ejrad.2019.108716

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  5 in total

1.  Automatic zonal segmentation of the prostate from 2D and 3D T2-weighted MRI and evaluation for clinical use.

Authors:  Dimitri Hamzaoui; Sarah Montagne; Raphaële Renard-Penna; Nicholas Ayache; Hervé Delingette
Journal:  J Med Imaging (Bellingham)       Date:  2022-03-14

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

Authors:  Sarah Montagne; Dimitri Hamzaoui; Alexandre Allera; Malek Ezziane; Anna Luzurier; Raphaelle Quint; Mehdi Kalai; Nicholas Ayache; Hervé Delingette; Raphaële Renard-Penna
Journal:  Insights Imaging       Date:  2021-06-05

3.  Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features.

Authors:  Lu Wang; Brendan Kelly; Edward H Lee; Hongmei Wang; Jimmy Zheng; Wei Zhang; Safwan Halabi; Jining Liu; Yulong Tian; Baoqin Han; Chuanbin Huang; Kristen W Yeom; Kexue Deng; Jiangdian Song
Journal:  Eur J Radiol       Date:  2021-01-15       Impact factor: 3.528

4.  Segmentation of the prostate, its zones, anterior fibromuscular stroma, and urethra on the MRIs and multimodality image fusion using U-Net model.

Authors:  Seyed Masoud Rezaeijo; Shabnam Jafarpoor Nesheli; Mehdi Fatan Serj; Mohammad Javad Tahmasebi Birgani
Journal:  Quant Imaging Med Surg       Date:  2022-10

5.  Harnessing clinical annotations to improve deep learning performance in prostate segmentation.

Authors:  Karthik V Sarma; Alex G Raman; Nikhil J Dhinagar; Alan M Priester; Stephanie Harmon; Thomas Sanford; Sherif Mehralivand; Baris Turkbey; Leonard S Marks; Steven S Raman; William Speier; Corey W Arnold
Journal:  PLoS One       Date:  2021-06-25       Impact factor: 3.240

  5 in total

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