Xu Lianghui, Halike Ayiguli, Sen Gan, Sha Mo
Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, Xinjiang, China.
Institute of Medical Engineering Interdisciplinary Research, Xinjiang Medical University, Urumqi, 830017, Xinjiang, China.
Sci Rep. 2025 May 25;15(1):18281. doi: 10.1038/s41598-025-02393-1.
In medical image segmentation, it is a challenging task to identify and locate the boundary of pathological tissue accurately. In response to this issue, this paper proposes a medical image segmentation model, named Local Enhancement Driven Global Optimization Network (LEGO-Net), and specially develops an Detail and Contour Recognition Module (DCRM) to accurately identify the boundaries of lesion tissue. Specifically, the DCRM has the following two main contributions: Firstly, it improves the network's capability to identify the boundaries of diseased tissue by examining the intricate spatial relationships between row and column elements on the feature map. Secondly, by integrating local modeling with global modeling, the network is able to not only capture the detailed local structural information of the lesion area but also take into account the tissue's overall structure, thereby enhancing the network's capability to delineate the boundaries of the lesion tissue more effectively. Furthermore, to further augment the network's capacity to discern lesion information, this paper introduces a Channel Feature Enhancement Module (CFEM). the CFEM can highlight the importance of elements that are beneficial to foreground feature discrimination. The outcomes demonstrate that the network architecture proposed in this paper is capable of effectively identifying and segmenting the boundaries of pathological tissues.
在医学图像分割中，准确识别和定位病理组织的边界是一项具有挑战性的任务。针对这一问题，本文提出了一种医学图像分割模型，名为局部增强驱动全局优化网络（LEGO-Net），并专门开发了一个细节与轮廓识别模块（DCRM）来准确识别病变组织的边界。具体而言，DCRM有以下两个主要贡献：首先，它通过检查特征图上各行各列元素之间复杂的空间关系，提高了网络识别病变组织边界的能力。其次，通过将局部建模与全局建模相结合，网络不仅能够捕捉病变区域详细的局部结构信息，还能考虑组织的整体结构，从而增强网络更有效地描绘病变组织边界的能力。此外，为了进一步增强网络辨别病变信息的能力，本文引入了一个通道特征增强模块（CFEM）。CFEM可以突出有利于前景特征辨别的元素的重要性。结果表明，本文提出的网络架构能够有效地识别和分割病理组织的边界。
