ArticleEuropean journal of radiology open2026
AI-based prostate volume estimation from multi-planar MRI under variable acquisition protocols.
Article in European journal of radiology open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Clinical pathways matter for multimodal deep learning in early Alzheimer's disease detection.Scientific reports · 2026Article
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Authors and funding
5 authors.
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Abstract
Background: Prostate MRI protocols vary across institutions, with abbreviated protocols increasingly limited to axial plane acquisitions. Conventional deep learning (DL) models for prostate volume (PV) estimation typically require complete availability of annotated full imaging protocols during training and inference, limiting their adaptability in real-world clinical workflows. This study aimed to develop and evaluate a knowledge-based (KB) DL segmentation model that adapts to variable MRI acquisition protocols, including axial-only abbreviated protocols. Methods: This retrospective study included 629 multiparametric 3-Tesla prostate MRI exams (66.60 ± 7.50 years) from biopsy-confirmed patients. Manual segmentations by expert radiologists and ellipsoid-derived volumes per PI-RADS 2.1 ( Results: With full multi-planar input, the KB model achieved a DSC of 0.91 ± 0.03 and RVD of 2.1 ± 6.4%, comparable to the fully supervised Conclusions: The proposed KB DL model enables accurate and flexible PV assessment under variable MRI protocols without requiring segmentation masks beyond the axial plane.
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