Evidence map›Paper›PMID 36185056›Full record

ArticleQuantitative imaging in medicine and surgery2022

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

Seyed Masoud Rezaeijo, Shabnam Jafarpoor Nesheli, Mehdi Fatan Serj, Mohammad Javad Tahmasebi Birgani

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Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
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36citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

36 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  4. Urethra contours on MRI: Multidisciplinary consensus educational atlas and reference standard for artificial intelligence benchmarking.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Seyed Masoud RezaeijoDepartment of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Shabnam Jafarpoor NesheliFaculty of Engineering, University of Science and Culture, Tehran, Iran.
Mehdi Fatan SerjDepartment of Computer Science and Mathematics of Security, Rovira i Virgili University, Tarragona, Spain.
Mohammad Javad Tahmasebi BirganiDepartment of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Due to the large variability in the prostate gland of different patient groups, manual segmentation is time-consuming and subject to inter-and intra-reader variations. Hence, we propose a U-Net model to automatically segment the prostate and its zones, including the peripheral zone (PZ), transitional zone (TZ), anterior fibromuscular stroma (AFMS), and urethra on the MRI [T2-weighted (T2W), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC)], and multimodality image fusion. Methods: A total of 91 eligible patients were retrospectively identified; 50 patients were considered for training process in a 10-fold cross-validation fashion and 41 ones for external test. Firstly, images were registered, and cropping was performed through a bounding box. In addition to T2W, DWI, and ADC separately, fused images were used. We considered three combinations, including T2W + DWI, T2W + ADC, and DWI + ADC, using wavelet transform. U-Net was applied to segment the prostate and its zones, AFMS, and urethra in a 10-fold cross-validation fashion. Eventually, dice score (DSC), intersection over union (IoU), precision, recall, and Hausdorff distance (HD) were used to evaluate the proposed model. Results: Using T2W images alone on the external test images, higher DSC, IoU, precision, and recall was achieved than the individual DWI and ADC images. DSC of 95%, 94%,98%, 94%, and 88%, IoU of 88%, 88.5%, 96%, 90%, and 79%, precision of 95.9%, 93.9%, 97.6%, 93.83%, and 87.82%, and recall of 94.2%, 94.2%, 98.3%, 94%, 87.93% was achieved for the whole prostate, PZ, TZ, urethra, and AFMS, respectively. The results clearly show that the best segmentation was obtained when the model is trained using T2W + DWI images. DSC of 99.06%, 99,05%, 99.04%, 99.09%, and 98.08%, IoU of 97.09%, 97.02%, 98.12%, 98.13%, and 96%, precision of 99.24%, 98.22%, 98.91%, 99.23%, and 98.9%, and recall of 98.3%, 99.8%, 99.02%, 98.93%, and 97.51% was achieved for the whole prostate, PZ, TZ, urethra, and AFMS, respectively. The min of the HD in the testing set for three combinations was 0.29 for the T2W + ADC procedure in the whole prostate class. Conclusions: Better performance was achieved using T2W + DWI images than T2W, DWI, and ADC separately or T2W + ADC and DWI + ADC in combination.

Indexed as

Automated segmentationimage fusionMRIprostateU-Net model

Identifiers

PMID36185056
PMCPMC9511435

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.