ArticleDiagnostics (Basel, Switzerland)2021
The Reproducibility of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review.Radiology. Artificial intelligence · 2024Pooled it
- mpMRI-Based Risk Estimation to Optimize Prostate Cancer Patient Selection for Active Surveillance.Cancers · 2026Article
- Reproducible meningioma grading across multi-center MRI protocols via hybrid radiomic and deep learning features.Neuroradiology · 2025Article
- Intra-Individual Reproducibility of Automated Abdominal Organ Segmentation-Performance of TotalSegmentator Compared to Human Readers and an Independent nnU-Net Model.Journal of imaging informatics in medicine · 2025Article
- Training and assessing convolutional neural network performance in automatic vascular segmentation using Ga-68 DOTATATE PET/CT.The international journal of cardiovascular imaging · 2024Article
- The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data.Zeitschrift fur medizinische Physik · 2024Review
- Algorithms for classification of sequences and segmentation of prostate gland: an external validation study.Abdominal radiology (New York) · 2024Article
- A modified U-Net convolutional neural network for segmenting periprostatic adipose tissue based on contour feature learning.Heliyon · 2024Article
- Artificial Intelligence-powered automatic volume calculation in medical images - available tools, performance and challenges for nuclear medicine.Nuklearmedizin. Nuclear medicine · 2023Review
- The impact of pre-processing and disease characteristics on reproducibility of T2-weighted MRI radiomics features.Magma (New York, N.Y.) · 2023Article
- Label-set impact on deep learning-based prostate segmentation on MRI.Insights into imaging · 2023Article
- Reproducibility analysis of automated deep learning based localisation of mandibular canals on a temporal CBCT dataset.Scientific reports · 2023Article
- Development and clinical utility analysis of a prostate zonal segmentation model on T2-weighted imaging: a multicenter study.Insights into imaging · 2023Article
- An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy.Frontiers in oncology · 2023Article
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7 authors.
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Abstract
Volume of interest segmentation is an essential step in computer-aided detection and diagnosis (CAD) systems. Deep learning (DL)-based methods provide good performance for prostate segmentation, but little is known about the reproducibility of these methods. In this work, an in-house collected dataset from 244 patients was used to investigate the intra-patient reproducibility of 14 shape features for DL-based segmentation methods of the whole prostate gland (WP), peripheral zone (PZ), and the remaining prostate zones (non-PZ) on T2-weighted (T2W) magnetic resonance (MR) images compared to manual segmentations. The DL-based segmentation was performed using three different convolutional neural networks (CNNs): V-Net, nnU-Net-2D, and nnU-Net-3D. The two-way random, single score intra-class correlation coefficient (ICC) was used to measure the inter-scan reproducibility of each feature for each CNN and the manual segmentation. We found that the reproducibility of the investigated methods is comparable to manual for all CNNs (14/14 features), except for V-Net in PZ (7/14 features). The ICC score for segmentation volume was found to be 0.888, 0.607, 0.819, and 0.903 in PZ; 0.988, 0.967, 0.986, and 0.983 in non-PZ; 0.982, 0.975, 0.973, and 0.984 in WP for manual, V-Net, nnU-Net-2D, and nnU-Net-3D, respectively. The results of this work show the feasibility of embedding DL-based segmentation in CAD systems, based on multiple T2W MR scans of the prostate, which is an important step towards the clinical implementation.
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