Clinical evaluation of nnU-Net-based segmentation for enhanced MRCP maximum intensity projection visualization

To evaluate whether deep learning-based segmentation can improve visualization and diagnostic interpretation of MR cholangiopancreatography (MRCP) maximum intensity projections (MIP) by suppressing overlapping high-intensity anatomy while preserving the pancreatobiliary system.

A total of 322 3D MRCP datasets from 162 patients were included. The training set comprised 265 cases from 127 patients, allowing multiple acquisitions per patient, whereas the evaluation set consisted of 35 cases with a single acquisition per patient. A deep learning-based segmentation model was trained using manual annotations with three distinct labels: background, primary structures, and secondary structures. Conservative safety margins were applied to reduce segmentation omissions. Two board-certified radiologists independently rated processed and original MIP images using 4-point Likert scales (1 = poor, 4 = excellent). Two-sided Wilcoxon signed-rank tests with Benjamini-Hochberg correction were used, and inter-reader agreement was assessed using linearly weighted Cohen's $\kappa$ .

Segmentation suppressed obscuring structures and improved biliary visualization. After correction for multiple comparisons, processed images showed significantly improved structure overlap scores compared with original images (3.5 ± 0.9 vs. 2.5 ± 0.6) and higher diagnostic confidence (3.4 ± 0.7 vs. 3.2 ± 0.7).

Deep learning-based segmentation improves MRCP MIP visualization by reducing anatomical overlap while preserving clinically relevant pancreatobiliary anatomy.