Challenges and solutions of deep learning-based automated liver segmentation: A systematic review

• Published in: Computers in Biology and Medicine
• Main Author: Ghobadi V.; Ismail L.I.; Wan Hasan W.Z.; Ahmad H.; Ramli H.R.; Norsahperi N.M.H.; Tharek A.; Hanapiah F.A.
• Format: Review
• Language: English
• Published: Elsevier Ltd 2025
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211072233&doi=10.1016%2fj.compbiomed.2024.109459&partnerID=40&md5=ccc41b70495083f6cd3f0a3399f260ee

The liver is one of the vital organs in the body. Precise liver segmentation in medical images is essential for liver disease treatment. The deep learning-based liver segmentation process faces several challenges. This research aims to analyze the challenges of liver segmentation in prior studies and identify the modifications made to network models and other enhancements implemented by researchers to tackle each challenge. In total, 88 articles from Scopus and ScienceDirect databases published between January 2016 and January 2022 have been studied. The liver segmentation challenges are classified into five main categories, each containing some subcategories. For each challenge, the proposed technique to overcome the challenge is investigated. The provided report details the authors, publication years, dataset types, imaging technologies, and evaluation metrics of all references for comparison. Additionally, a summary table outlines the challenges and solutions. © 2024