Abstract

Deep learning segmentation techniques have been created recently to enhance the identification of liver illnesses, which have demonstrated tremendous promise for increasing precision and effectiveness in diagnosis. Here discuss and evaluate recent studies on deep learning segmentation techniques for the liver utilizing MRI, CT, and ultrasound data. This review concentrates on methods for segmenting the pancreas, biliary tract, liver and gallbladder precisely since they are crucial for clinical diagnosis. Here aim to highlight the major trends and problems in this field through our examination of the literature, and to offer insights into the possibilities and difficulties for additional study and research in this sector.