Wan Yuchai, Zhang Lili, Wang Murong
Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China.
Femtosecond Applications and Research, Science and Technology Innovation Base, No. 80, Lanyue Road, Science City, High-Tech Industrial Development Zone, Huangpu District, Guangzhou 510700, China.
Entropy (Basel). 2025 Jul 9;27(7):735. doi: 10.3390/e27070735.
The deep learning-based analysis of liver CT images is expected to provide assistance for clinicians in the diagnostic decision-making process. However, the accuracy of existing methods still falls short of clinical requirements and needs to be further improved. Therefore, in this work, we propose a novel multi-level hierarchical framework for liver tumor segmentation. In the first level, we integrate inter-slice spatial information by a 2.5D network to resolve the accuracy-efficiency trade-off inherent in conventional 2D/3D segmentation strategies for liver tumor segmentation. Then, the second level extracts the inner-slice global and local features for enhancing feature representation. We propose the Res-Inception-SE Block, which combines residual connections, multi-scale Inception modules, and squeeze-excitation attention to capture comprehensive global and local features. Furthermore, we design a hybrid loss function combining Binary Cross Entropy (BCE) and Dice loss to solve the category imbalance problem and accelerate convergence. Extensive experiments on the LiTS17 dataset demonstrate the effectiveness of our method on accuracy, efficiency, and visual results for liver tumor segmentation.
基于深度学习的肝脏CT图像分析有望在诊断决策过程中为临床医生提供帮助。然而，现有方法的准确性仍未达到临床要求，需要进一步提高。因此，在这项工作中，我们提出了一种用于肝脏肿瘤分割的新型多级分层框架。在第一级，我们通过一个2.5D网络整合切片间空间信息，以解决传统2D/3D肝脏肿瘤分割策略中固有的准确性-效率权衡问题。然后，第二级提取切片内全局和局部特征以增强特征表示。我们提出了Res-Inception-SE模块，它结合了残差连接、多尺度Inception模块和挤压-激励注意力来捕获全面的全局和局部特征。此外，我们设计了一种结合二元交叉熵（BCE）和Dice损失的混合损失函数来解决类别不平衡问题并加速收敛。在LiTS17数据集上进行的大量实验证明了我们的方法在肝脏肿瘤分割的准确性、效率和视觉效果方面的有效性。
