Magnetic resonance in medicine · 2026
Automated Coregistered Segmentation for Volumetric Analysis of Multiparametric Renal MRI.
СтатьяНазвание и аннотация: Перевод в очереди
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Ghoul Aya, Liang Cecilia, Loster Isabelle, Umapathy Lavanya, Kühn Bernd, Martirosian Petros, Seith Ferdinand, Gatidis Sergios, Küstner Thomas
Аннотация
PURPOSE: This study aims to develop and evaluate a fully automated deep learning-driven postprocessing pipeline for multiparametric renal MRI, enabling accurate kidney alignment, segmentation, and quantitative feature extraction within a single efficient workflow.
METHODS: Our method has three main stages. First, a segmentation network delineates renal structures in high-contrast images. Next, a deep learning-based pairwise image registration algorithm maps the multiparametric image series to a common target and transfers the predicted annotations between the multiparametric images. Finally, clinically relevant quantitative parameters are extracted through region-specific assessment of renal structure and function based on the aligned and segmented multiparametric data. We used five-fold cross-validation to compare the segmentation outcomes and extracted features with manual analyses in 24 patients with prostate cancer or neuroendocrine tumors and 10 healthy subjects, each undergoing repeated scans.
RESULTS: Our automated pipeline achieved high agreement with expert kidney segmentation while delivering significant alignment improvements through registration. Volumetric analysis showed a strong correlation (r > 0.9) with manual results, and feature extraction demonstrated high intraclass correlation coefficients with minimal bias. The complete processing pipeline, encompassing coregistration, segmentation, and feature extraction, required approximately 15 s per scan from raw input to final quantitative output.
CONCLUSION: The study establishes a reliable automated pipeline for renal multiparametric MRI postprocessing. The achieved accuracy and efficiency can support improved diagnosis and treatment planning for patients with kidney disease.
METHODS: Our method has three main stages. First, a segmentation network delineates renal structures in high-contrast images. Next, a deep learning-based pairwise image registration algorithm maps the multiparametric image series to a common target and transfers the predicted annotations between the multiparametric images. Finally, clinically relevant quantitative parameters are extracted through region-specific assessment of renal structure and function based on the aligned and segmented multiparametric data. We used five-fold cross-validation to compare the segmentation outcomes and extracted features with manual analyses in 24 patients with prostate cancer or neuroendocrine tumors and 10 healthy subjects, each undergoing repeated scans.
RESULTS: Our automated pipeline achieved high agreement with expert kidney segmentation while delivering significant alignment improvements through registration. Volumetric analysis showed a strong correlation (r > 0.9) with manual results, and feature extraction demonstrated high intraclass correlation coefficients with minimal bias. The complete processing pipeline, encompassing coregistration, segmentation, and feature extraction, required approximately 15 s per scan from raw input to final quantitative output.
CONCLUSION: The study establishes a reliable automated pipeline for renal multiparametric MRI postprocessing. The achieved accuracy and efficiency can support improved diagnosis and treatment planning for patients with kidney disease.