ArticleComputational and mathematical methods in medicine2020
Evaluation of Multimodal Algorithms for the Segmentation of Multiparametric MRI Prostate Images.
Article in Computational and mathematical methods in medicine, 2020. 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
- A clinically informed automated evaluation pipeline for medical image segmentation based on Medical Similarity Index.Physics and imaging in radiation oncology · 2026Article
- Prostate cancer and benign prostatic hyperplasia lesions segmentation using diffusion kurtosis imaging, T2*, and R2* mapping with U-Net++ algorithm.Radiological physics and technology · 2026Article
- A Neural Conditional Random Field Model Using Deep Features and Learnable Functions for End-to-End MRI Prostate Zonal Segmentation.The journal of machine learning for biomedical imaging · 2025Article
- The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: a narrative reviewDiagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- RaNet: a residual attention network for accurate prostate segmentation in T2-weighted MRI.Frontiers in medicine · 2025Article
- Transition-zone PSA-density calculated from MRI deep learning prostate zonal segmentation model for prediction of clinically significant prostate cancer.Abdominal radiology (New York) · 2024Article
- Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management.Cancers · 2024Review
- Development and clinical utility analysis of a prostate zonal segmentation model on T2-weighted imaging: a multicenter study.Insights into imaging · 2023Article
- CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI.IEEE transactions on medical imaging · 2023Article
- Automatic segmentation of prostate zonal anatomy on MRI: a systematic review of the literature.Insights into imaging · 2022Article
- Combined model-based and deep learning-based automated 3D zonal segmentation of the prostate on T2-weighted MR images: clinical evaluation.European radiology · 2022Article
- Artificial Intelligence Segmentation Algorithm-Based Optical Coherence Tomography Image in Evaluation of Binocular Retinopathy.Computational and mathematical methods in medicine · 2022Article
- Evaluation of deep learning-based multiparametric MRI oropharyngeal primary tumor auto-segmentation and investigation of input channel effects: Results from a prospective imaging registry.Clinical and translational radiation oncology · 2022Article
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11 authors.
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Abstract
Prostate segmentation in multiparametric magnetic resonance imaging (mpMRI) can help to support prostate cancer diagnosis and therapy treatment. However, manual segmentation of the prostate is subjective and time-consuming. Many deep learning monomodal networks have been developed for automatic whole prostate segmentation from T2-weighted MR images. We aimed to investigate the added value of multimodal networks in segmenting the prostate into the peripheral zone (PZ) and central gland (CG). We optimized and evaluated monomodal DenseVNet, multimodal ScaleNet, and monomodal and multimodal HighRes3DNet, which yielded dice score coefficients (DSC) of 0.875, 0.848, 0.858, and 0.890 in WG, respectively. Multimodal HighRes3DNet and ScaleNet yielded higher DSC with statistical differences in PZ and CG only compared to monomodal DenseVNet, indicating that multimodal networks added value by generating better segmentation between PZ and CG regions but did not improve the WG segmentation. No significant difference was observed in the apex and base of WG segmentation between monomodal and multimodal networks, indicating that the segmentations at the apex and base were more affected by the general network architecture. The number of training data was also varied for DenseVNet and HighRes3DNet, from 20 to 120 in steps of 20. DenseVNet was able to yield DSC of higher than 0.65 even for special cases, such as TURP or abnormal prostate, whereas HighRes3DNet's performance fluctuated with no trend despite being the best network overall. Multimodal networks did not add value in segmenting special cases but generally reduced variations in segmentation compared to the same matched monomodal network.
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