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Challenges and solutions of deep learning-based automated liver segmentation: A systematic review
Vahideh Ghobadi1, Luthffi Idzhar Ismail1, Wan Zuha Wan Hasan1
1Faculty of Engineering, Universiti Putra Malaysia, Serdang, 43400, Selangor, Malaysia.
Computers in Biology and Medicine
|December 6, 2024
Summary
This study reviews deep learning challenges in liver segmentation for medical imaging. It analyzes 88 articles to identify solutions for improving liver segmentation accuracy in disease treatment.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer Vision
Background:
- Accurate liver segmentation in medical images is crucial for diagnosing and treating liver diseases.
- Deep learning models are widely used for liver segmentation but face significant challenges.
- Existing research has explored various modifications to address these segmentation difficulties.
Conclusions:
- Understanding and addressing the identified challenges is key to advancing deep learning for liver segmentation.
- The review provides a valuable resource for researchers seeking effective strategies to improve liver segmentation accuracy.
- Future research can build upon these identified solutions to develop more robust and precise liver segmentation methods.
