  This article presents an evaluation of biliary tract segmentation methods
used for 3D reconstruction, which may be very usefull in various critical
interventions, such as endoscopic retrograde cholangiopancreatography (ERCP),
using the 3D Slicer software. This article provides an assessment of biliary
tract segmentation techniques employed for 3D reconstruction, which can prove
highly valuable in diverse critical procedures like endoscopic retrograde
cholangiopancreatography (ERCP) through the utilization of 3D Slicer software.
Three different methods, namely thresholding, flood filling, and region
growing, were assessed in terms of their advantages and disadvantages. The
study involved 10 patient cases and employed quantitative indices and
qualitative evaluation to assess the segmentations obtained by the different
segmentation methods against ground truth. The results indicate that the
thresholding method is almost manual and time-consuming, while the flood
filling method is semi-automatic and also time-consuming. Although both methods
improve segmentation quality, they are not reproducible. Therefore, an
automatic method based on region growing was developed to reduce segmentation
time, albeit at the expense of quality. These findings highlight the pros and
cons of different conventional segmentation methods and underscore the need to
explore alternative approaches, such as deep learning, to optimize biliary
tract segmentation in the context of ERCP.
