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 an...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/115246/ http://psasir.upm.edu.my/id/eprint/115246/1/115246.pdf |
| _version_ | 1848866725131452416 |
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| author | Ghobadi, Vahideh Ismail, Luthffi Idzhar Wan Hasan, Wan Zuha Ahmad, Haron Ramli, Hafiz Rashidi Norsahperi, Nor Mohd Haziq Tharek, Anas Hanapiah, Fazah Akhtar |
| author_facet | Ghobadi, Vahideh Ismail, Luthffi Idzhar Wan Hasan, Wan Zuha Ahmad, Haron Ramli, Hafiz Rashidi Norsahperi, Nor Mohd Haziq Tharek, Anas Hanapiah, Fazah Akhtar |
| author_sort | Ghobadi, Vahideh |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T14:25:10Z |
| format | Article |
| id | upm-115246 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:25:10Z |
| publishDate | 2025 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1152462025-03-12T04:35:10Z http://psasir.upm.edu.my/id/eprint/115246/ Challenges and solutions of deep learning-based automated liver segmentation: a systematic review Ghobadi, Vahideh Ismail, Luthffi Idzhar Wan Hasan, Wan Zuha Ahmad, Haron Ramli, Hafiz Rashidi Norsahperi, Nor Mohd Haziq Tharek, Anas Hanapiah, Fazah Akhtar 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. Elsevier 2025-02 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/115246/1/115246.pdf Ghobadi, Vahideh and Ismail, Luthffi Idzhar and Wan Hasan, Wan Zuha and Ahmad, Haron and Ramli, Hafiz Rashidi and Norsahperi, Nor Mohd Haziq and Tharek, Anas and Hanapiah, Fazah Akhtar (2025) Challenges and solutions of deep learning-based automated liver segmentation: a systematic review. Computers in Biology and Medicine, 185. art. no. 109459. pp. 1-15. ISSN 0010-4825; eISSN: 1879-0534 https://www.sciencedirect.com/science/article/pii/S0010482524015440?via%3Dihub 10.1016/j.compbiomed.2024.109459 |
| spellingShingle | Ghobadi, Vahideh Ismail, Luthffi Idzhar Wan Hasan, Wan Zuha Ahmad, Haron Ramli, Hafiz Rashidi Norsahperi, Nor Mohd Haziq Tharek, Anas Hanapiah, Fazah Akhtar Challenges and solutions of deep learning-based automated liver segmentation: a systematic review |
| title | Challenges and solutions of deep learning-based automated liver segmentation: a systematic review |
| title_full | Challenges and solutions of deep learning-based automated liver segmentation: a systematic review |
| title_fullStr | Challenges and solutions of deep learning-based automated liver segmentation: a systematic review |
| title_full_unstemmed | Challenges and solutions of deep learning-based automated liver segmentation: a systematic review |
| title_short | Challenges and solutions of deep learning-based automated liver segmentation: a systematic review |
| title_sort | challenges and solutions of deep learning-based automated liver segmentation: a systematic review |
| url | http://psasir.upm.edu.my/id/eprint/115246/ http://psasir.upm.edu.my/id/eprint/115246/ http://psasir.upm.edu.my/id/eprint/115246/ http://psasir.upm.edu.my/id/eprint/115246/1/115246.pdf |