背景:脊柱X射线图像中椎骨的自动分割对于临床诊断至关重要,案例分析,和脊柱病变的手术计划。
目标:然而,由于X射线图像的固有特性,包括低对比度,高噪音,和不均匀的灰度,在计算机辅助脊柱图像分析和疾病诊断应用中,它仍然是一个关键和具有挑战性的问题。
方法:在本文中,多尺度特征增强网络(MFENet),被提议用于分割整个脊柱X射线图像,帮助医生诊断脊柱相关疾病。为了增强特征提取,该网络包含双分支特征提取模块(DFEM)和语义聚合模块(SAM)。DFEM具有并行双分支结构。上分支利用多尺度卷积内核从图像中提取特征。采用不同大小的卷积内核有助于捕获不同尺度的细节和结构信息。下部分支包含注意力机制以进一步优化特征表示。通过对空间和跨通道的特征图进行建模,网络变得更加专注于关键特征区域,并抑制与任务无关的信息。SAM利用上下文语义信息来补偿在池化和卷积运算期间丢失的细节。它集成了来自不同尺度的高级特征信息,以减少分割结果的不连续性。此外,采用混合损失函数来增强网络的特征提取能力。
结果:在这项研究中,我们利用河南省人民医院脊柱外科提供的数据集进行了大量实验。实验结果表明,与其他先进方法相比,我们提出的MFENet在X射线图像上的脊柱分割中显示出更好的分割性能。MIOU达到92.61±0.431,DSC为92.42±0.329,全局精度为99.51±0.037。
结论:我们的模型能够更有效地学习和提取全局上下文语义信息,显著改善脊柱分割性能,进一步帮助医生分析病人的情况。
BACKGROUND: Automatic segmentation of vertebrae in spinal x-ray images is crucial for clinical diagnosis, case analysis, and surgical planning of spinal lesions.
OBJECTIVE: However, due to the inherent characteristics of x-ray images, including low contrast, high noise, and uneven grey scale, it remains a critical and challenging problem in computer-aided spine image analysis and disease diagnosis applications.
METHODS: In this paper, a Multiscale Feature Enhancement Network (MFENet), is proposed for segmenting whole spinal x-ray images, to aid doctors in diagnosing spinal-related diseases. To enhance feature extraction, the network incorporates a Dual-branch Feature Extraction Module (DFEM) and a Semantic Aggregation Module (SAM). The DFEM has a parallel dual-branch structure. The upper branch utilizes multiscale convolutional kernels to extract features from images. Employing convolutional kernels of different sizes helps capture details and structural information at different scales. The lower branch incorporates attention mechanisms to further optimize feature representation. By modeling the feature maps spatially and across channels, the network becomes more focused on key feature regions and suppresses task-irrelevant information. The SAM leverages contextual semantic information to compensate for details lost during pooling and convolution operations. It integrates high-level feature information from different scales to reduce segmentation result discontinuity. In addition, a hybrid loss function is employed to enhance the network\'s feature extraction capability.
RESULTS: In this study, we conducted a multitude of experiments utilizing dataset provided by the Spine Surgery Department of Henan Provincial People\'s Hospital. The experimental results indicate that our proposed MFENet demonstrates superior segmentation performance in spinal segmentation on x-ray images compared to other advanced methods, achieving 92.61 ± 0.431 for MIoU, 92.42 ± 0.329 for DSC, and 99.51 ± 0.037 for Global_accuracy.
CONCLUSIONS: Our model is able to more effectively learn and extract global contextual semantic information, significantly improving spinal segmentation performance, further aiding doctors in analyzing patient conditions.