IF 7 2区 医学 Q1 BIOLOGY
Vahideh Ghobadi, Luthffi Idzhar Ismail, Wan Zuha Wan Hasan, Haron Ahmad, Hafiz Rashidi Ramli, Nor Mohd Haziq Norsahperi, Anas Tharek, Fazah Akhtar Hanapiah
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引用次数: 0

摘要

肝脏是人体的重要器官之一。医学图像中精确的肝脏分割对于肝病治疗至关重要。基于深度学习的肝脏分割过程面临着一些挑战。本研究旨在分析以往研究中肝脏分割所面临的挑战,并找出研究人员为应对每个挑战而对网络模型所做的修改和其他改进。共研究了 Scopus 和 ScienceDirect 数据库中 2016 年 1 月至 2022 年 1 月间发表的 88 篇文章。肝脏分割挑战分为五大类,每一类又包含一些子类。针对每个挑战,研究了克服挑战的建议技术。报告详细列出了所有参考文献的作者、发表年份、数据集类型、成像技术和评估指标,以供比较。此外,汇总表还概述了挑战和解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Challenges and solutions of deep learning-based automated liver segmentation: A systematic review.

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.

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来源期刊
Computers in biology and medicine
Computers in biology and medicine 工程技术-工程:生物医学
CiteScore
11.70
自引率
10.40%
发文量
1086
审稿时长
74 days
期刊介绍: Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.
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