Song Ziwei, Wu Weiwei, Wu Shuicai
Department of Biomedical Engineering, College of Chemistry and Life Sciences, Beijing University of Technology, Beijing 100124, China.
College of Biomedical Engineering, Capital Medical University, Beijing 100069, China.
Sensors (Basel). 2025 Mar 14;25(6):1814. doi: 10.3390/s25061814.
Accurate segmentation of the liver and liver tumors is crucial for clinical diagnosis and treatment. However, the task poses significant challenges due to the complex morphology of tumors, indistinct features of small targets, and the similarity in grayscale values between the liver and surrounding organs. To address these issues, this paper proposes an enhanced 3D UNet architecture, named ELANRes-MSCA-UNet. By incorporating a structural re-parameterized residual module (ELANRes) and a multi-scale convolutional attention module (MSCA), the network significantly improves feature extraction and boundary optimization, particularly excelling in segmenting small targets. Additionally, a two-stage strategy is employed, where the liver region is segmented first, followed by the fine-grained segmentation of tumors, effectively reducing false positive rates. Experiments conducted on the LiTS2017 dataset demonstrate that the ELANRes-MSCA-UNet achieved Dice scores of 97.2% and 72.9% for liver and tumor segmentation tasks, respectively, significantly outperforming other state-of-the-art methods. These results validate the accuracy and robustness of the proposed method in medical image segmentation and highlight its potential for clinical applications.
肝脏和肝脏肿瘤的精确分割对于临床诊断和治疗至关重要。然而，由于肿瘤形态复杂、小目标特征不明显以及肝脏与周围器官灰度值相似，该任务面临重大挑战。为解决这些问题，本文提出了一种增强的3D UNet架构，名为ELANRes-MSCA-UNet。通过结合结构重参数化残差模块（ELANRes）和多尺度卷积注意力模块（MSCA），该网络显著提高了特征提取和边界优化能力，尤其在分割小目标方面表现出色。此外，采用了两阶段策略，先分割肝脏区域，然后对肿瘤进行细粒度分割，有效降低了误报率。在LiTS2017数据集上进行的实验表明，ELANRes-MSCA-UNet在肝脏和肿瘤分割任务中的Dice分数分别达到了97.2%和72.9%，显著优于其他现有方法。这些结果验证了所提方法在医学图像分割中的准确性和鲁棒性，并突出了其在临床应用中的潜力。
