Polattimur Rukiye, Yıldırım Mehmet Süleyman, Dandıl Emre
Department of Electronics and Computer Engineering, Institute of Graduate, Bilecik Seyh Edebali University, 11230 Bilecik, Türkiye.
Department of Computer Technology, Söğüt Vocational School, Bilecik Şeyh Edebali University, Sögüt, 11600 Bilecik, Türkiye.
Diagnostics (Basel). 2025 Apr 19;15(8):1041. doi: 10.3390/diagnostics15081041.
: Multiple sclerosis (MS) is an autoimmune disease that damages the myelin sheath of the central nervous system, which includes the brain and spinal cord. Although MS lesions in the brain are more frequently investigated, MS lesions in the cervical spinal cord (CSC) can be much more specific for the diagnosis of the disease. Furthermore, as lesion burden in the CSC is directly related to disease progression, the presence of lesions in the CSC may help to differentiate MS from other neurological diseases. : In this study, two novel deep learning models based on fractal architectures are proposed for the automatic detection and segmentation of MS lesions in the CSC by improving the convolutional and connection structures used in the layers of the U-Net architecture. In our previous study, we introduced the FractalSpiNet architecture by incorporating fractal convolutional block structures into the U-Net framework to develop a deeper network for segmenting MS lesions in the CPC. In this study, to improve the detection of smaller structures and finer details in the images, an attention mechanism is integrated into the FractalSpiNet architecture, resulting in the Att-FractalSpiNet model. In addition, in the second hybrid model, a fractal convolutional block is incorporated into the skip connection structure of the U-Net architecture, resulting in the development of the Con-FractalU-Net model. Experimental studies were conducted using U-Net, FractalSpiNet, Con-FractalU-Net, and Att-FractalSpiNet architectures to detect the CSC region and the MS lesions within its boundaries. In segmenting the CSC region, the proposed Con-FractalU-Net architecture achieved the highest Dice Similarity Coefficient (DSC) score of 98.89%. Similarly, in detecting MS lesions within the CSC region, the Con-FractalU-Net model again achieved the best performance with a DSC score of 91.48%. : For segmentation of the CSC region and detection of MS lesions, the proposed fractal-based Con-FractalU-Net and Att-FractalSpiNet architectures achieved higher scores than the baseline U-Net architecture, particularly in segmenting small and complex structures.
多发性硬化症（MS）是一种自身免疫性疾病，会损害中枢神经系统的髓鞘，中枢神经系统包括大脑和脊髓。虽然大脑中的MS病变更常被研究，但颈脊髓（CSC）中的MS病变对该疾病的诊断可能更具特异性。此外，由于CSC中的病变负担与疾病进展直接相关，CSC中病变的存在可能有助于将MS与其他神经系统疾病区分开来。：在本研究中，提出了两种基于分形架构的新型深度学习模型，通过改进U-Net架构各层中使用的卷积和连接结构，用于自动检测和分割CSC中的MS病变。在我们之前的研究中，我们通过将分形卷积块结构纳入U-Net框架来引入FractalSpiNet架构，以开发一个更深的网络来分割CPC中的MS病变。在本研究中，为了改善对图像中较小结构和更精细细节的检测，将注意力机制集成到FractalSpiNet架构中，从而产生了Att-FractalSpiNet模型。此外，在第二个混合模型中，将分形卷积块纳入U-Net架构的跳跃连接结构中，从而开发出Con-FractalU-Net模型。使用U-Net、FractalSpiNet、Con-FractalU-Net和Att-FractalSpiNet架构进行了实验研究，以检测CSC区域及其边界内的MS病变。在分割CSC区域时，所提出的Con-FractalU-Net架构实现了最高的骰子相似系数（DSC）得分，为98.89%。同样，在检测CSC区域内的MS病变时，Con-FractalU-Net模型再次表现最佳，DSC得分为91.48%。：对于CSC区域分割和MS病变检测，所提出的基于分形的Con-FractalU-Net和Att-FractalSpiNet架构比基线U-Net架构获得了更高的分数，特别是在分割小而复杂的结构方面。
