Abstract
Magnetic resonance cholangiopancreatography (MRCP), an MRI-based technique for imaging the bile and pancreatic ducts, plays a vital role in the investigation of pancreatobiliary diseases. In current clinical practice, MRCP image interpretation remains primarily qualitative, though there is growing interest in using quantitative biomarkers, computed from segmentations of the biliary tree, to provide more objective assessments. The variable image quality and duct contrasts in MRCP images, as well as the presence of bifurcations, tortuous bile ducts and bright gastrointestinal structures, makes segmenting the biliary tree from MRCP images a challenging task. We propose a method, based on the monogenic signal, for detecting the biliary tree in MRCP images. Using both phantom and clinical data we show that by tuning the monogenic signal to detect symmetric features we can successfully detect bile ducts and obtain accurate duct diameter measurements. Compared to the Hessian-based Frangi vesselness filter, we show that our method gives superior background noise suppression and performs better at duct bifurcations, where the model assumptions underlying vesselness fail.
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Ralli, G.P., Ridgway, G.R., Brady, S.M. (2020). Segmentation of the Biliary Tree from MRCP Images via the Monogenic Signal. In: Papież, B., Namburete, A., Yaqub, M., Noble, J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham. https://doi.org/10.1007/978-3-030-52791-4_9
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DOI: https://doi.org/10.1007/978-3-030-52791-4_9
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