Keywords: Analysis/Processing, Biliary, Pancreas
Motivation: In Magnetic Resonance Cholangiopancreatography (MRCP), maximum-intensity projection images are created to enhance duct structures, but areas in the image that interfere with a diagnosis must be manually removed. Automating this process would greatly reduce manual workload.
Goal(s): To develop a deep learning model that automatically removes unnecessary areas in MRCP images, reducing manual processing and speeding up diagnosis.
Approach: We evaluated four approaches using two schemes (segmentation and MRCP-Specific Adaptive Volume Clipping (MAVC)) and two deep neural networks (U-Net and Transformer).
Results: The MAVC/Transformer model achieved the best performance, effectively removing unnecessary areas in MRCP images and enhancing diagnostic efficiency.
Impact: The MRCP-Specific Adaptive Volume Clipping method enhances MRCP examination efficiency by automatically removing unnecessary regions in maximum-intensity projection images of MRCP. This approach reduces manual workload, offering flexibility without precise segmentation, and improves workflow and diagnostic accuracy.
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