Dongguan Polytechnic.
Electronic Information School, Dongguan Polytechnic, Dongguan 523808, China.
Crit Rev Biomed Eng. 2024;52(4):1-15. doi: 10.1615/CritRevBiomedEng.2024052258.
Computer assisted diagnostic technology has been widely used in clinical practice, specifically focusing on medical image segmentation. Its purpose is to segment targets with certain special meanings in medical images and extract relevant features, providing reliable basis for subsequent clinical diagnosis and research. However, because of different shapes and complex structures of segmentation targets in different medical images, some imaging techniques have similar characteristics, such as intensity, color, or texture, for imaging different organs and tissues. The localization and segmentation of targets in medical images remains an urgent technical challenge to be solved. As such, an improved full scale skip connection network structure for the CT liver image segmentation task is proposed. This structure includes a biomimetic attention module between the shallow encoder and the deep decoder, and the feature fusion proportion coefficient between the two is learned to enhance the attention of the overall network to the segmented target area. In addition, based on the traditional point sampling mechanism, an improved point sampling strategy is proposed for characterizing medical images to further enhance the edge segmentation effect of CT liver targets. The experimental results on the commonly used combined (CT-MR) health absolute organ segmentation (CHAOS) dataset show that the average dice similarity coefficient (DSC) can reach 0.9467, the average intersection over union (IOU) can reach 0.9623, and the average F1 score can reach 0.9351. This indicates that the model can effectively learn image detail features and global structural features, leading to improved segmentation of liver images.
计算机辅助诊断技术已广泛应用于临床实践，特别是在医学图像分割方面。其目的是分割医学图像中具有特定特殊含义的目标，并提取相关特征，为后续的临床诊断和研究提供可靠的依据。但是，由于不同医学图像中分割目标的形状和结构复杂，一些成像技术具有相似的特征，例如强度、颜色或纹理，用于对不同的器官和组织进行成像。因此，医学图像中目标的定位和分割仍然是一个亟待解决的技术挑战。为此，提出了一种用于 CT 肝脏图像分割任务的改进的全尺度 skip 连接网络结构。该结构在浅层编码器和深层解码器之间包含一个仿生注意模块，并且学习两个模块之间的特征融合比例系数，以增强网络对分割目标区域的注意力。此外，基于传统的点采样机制，提出了一种改进的点采样策略，用于对医学图像进行特征刻画，以进一步增强 CT 肝脏目标的边缘分割效果。在常用的联合（CT-MR）健康绝对器官分割（CHAOS）数据集上的实验结果表明，平均骰子相似系数（DSC）可达 0.9467，平均交并比（IOU）可达 0.9623，平均 F1 分数可达 0.9351。这表明该模型能够有效地学习图像的细节特征和全局结构特征，从而提高肝脏图像的分割效果。
