Tinglan Lu, Jun Qin, Guihe Qin, Weili Shi, Wentao Zhang
Changchun University of Science and Technology, Changchun, China.
Jilin University, Changchun, China.
Sci Rep. 2025 Jan 3;15(1):683. doi: 10.1038/s41598-024-78917-y.
Due to the low contrast of abdominal CT (Computer Tomography) images and the similar color and shape of the liver to other organs such as the spleen, stomach, and kidneys, liver segmentation presents significant challenges. Additionally, 2D CT images obtained from different angles (such as sagittal, coronal, and transverse planes) increase the diversity of liver morphology and the complexity of segmentation. To address these issues, this paper proposes a Detail Enhanced Convolution (DE Conv) to improve liver feature learning and thereby enhance liver segmentation performance. Furthermore, to enable the model to better learn liver features at different scales, a Multi-Scale Feature Fusion module (MSFF) is added to the skip connections in the model. The MSFF module enhances the capture of global features, thus improving the accuracy of the liver segmentation model. Through the aforementioned research, this paper proposes a liver segmentation network based on detail enhancement and multi-scale feature fusion (DEMF-Net). We conducted extensive experiments on the LiTS17 dataset, and the results demonstrate that the DEMF-Net model achieved significant improvements across various evaluation metrics. Therefore, the proposed DEMF-Net model can achieve precise liver segmentation.
由于腹部CT（计算机断层扫描）图像的对比度较低，且肝脏与脾脏、胃和肾脏等其他器官的颜色和形状相似，肝脏分割面临重大挑战。此外，从不同角度（如矢状面、冠状面和横断面）获得的二维CT图像增加了肝脏形态的多样性和分割的复杂性。为了解决这些问题，本文提出了一种细节增强卷积（DE Conv）来改进肝脏特征学习，从而提高肝脏分割性能。此外，为了使模型能够更好地学习不同尺度的肝脏特征，在模型的跳跃连接中添加了一个多尺度特征融合模块（MSFF）。MSFF模块增强了全局特征的捕获，从而提高了肝脏分割模型的准确性。通过上述研究，本文提出了一种基于细节增强和多尺度特征融合的肝脏分割网络（DEMF-Net）。我们在LiTS17数据集上进行了广泛的实验，结果表明DEMF-Net模型在各种评估指标上都取得了显著的改进。因此，所提出的DEMF-Net模型能够实现精确的肝脏分割。
