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Automatic Liver Segmentation Using EfficientNet and Attention-Based Residual U-Net in CT
Jinke Wang1,2, Xiangyang Zhang3, Peiqing Lv3
1Department of Software Engineering, Harbin University of Science and Technology, No. 2006, Xueyuan Road, Shandong Province, Rongcheng City, 264300, China. jkwang@hitwh.edu.cn.
Journal of Digital Imaging
|June 17, 2022
Summary
This study introduces an advanced deep learning framework for precise automatic liver segmentation. The novel method enhances feature extraction and accuracy, showing promising results in computer-assisted liver segmentation tasks.
Area of Science:
- Medical Imaging
- Computer Vision
- Deep Learning
Background:
- Accurate liver segmentation is crucial for medical diagnosis and treatment planning.
- Existing methods face challenges in feature extraction and segmentation accuracy.
Purpose of the Study:
- To develop and validate a novel network framework for automatic and accurate liver segmentation.
- To improve upon existing deep learning models for liver segmentation tasks.
Main Methods:
- Utilized EfficientNetB4 as an encoder for enhanced feature extraction.
- Incorporated an attention gate in skip connections to focus on relevant features.
- Employed residual blocks in the decoder to mitigate gradient vanishing and boost accuracy.
Main Results:
- Achieved superior segmentation performance on the SLiver07 dataset across all five standard metrics.
- Demonstrated competitive results on the LiTS17 dataset, with minor inferiority only in RVD.
- Qualitative and quantitative analyses confirmed the method's effectiveness and applicability.
Conclusions:
- The proposed framework offers a robust solution for automatic liver segmentation.
- The integration of EfficientNetB4, attention gates, and residual learning significantly enhances segmentation accuracy.
- The method shows strong potential for computer-assisted liver segmentation in clinical settings.
