【摘要】 CT图像中的腰椎分割对腰椎疾病的辅助诊断与治疗具有重要意义，U-Net及其扩展模型在医学图像分割领域受到广泛关注。针对Rolling U-Net模型在腰椎分割中细节特征易丢失等问题，提出一种改进的Rolling U-Net模型用于腰椎CT图像分割。该模型是一种结合多层感知器（MLP）的卷积神经网络（CNN）模型，在第4层卷积层和瓶颈层处插入特征激励模块以增加关键解剖结构的权重，通过构建长距离-局部块（Lo2 block）实现局部特征信息与长距离依赖关系的融合，Lo2 block的核心R-MLP模块用来学习整个图像在单一方向上的长距离依赖关系，通过控制和组合不同方向的R-MLP模块构建出OR-MLP和DOR-MLP模块，以捕捉多个方向上的长距离依赖关系，最后融入残差卷积恢复腰椎分割细节；同时将Dice损失函数与交叉熵损失函数的像素分类优势相结合设计了MultiClassDiceCE损失函数。实验结果表明：类别数与采样策略对改进Rolling U-Net模型的分割性能有显著影响，二元分割任务宜采用按图片总数量的划分策略以兼顾精度与稳定性，多分类任务更适合采用按实例总数量的划分策略；在JST＿LV和VerSe数据集上进行多元分割任务时，改进Rolling U-Net模型的平均交并比（IoU）、Dice系数、召回率、特异度、精确度均优于U-Net、Attention U-Net、Rolling U-Net等分割模型，表明改进的模型可有效提升腰椎CT图像分割的准确性、细节完整性与分类鲁棒性。更多还原

【Abstract】 Segmentation of lumbar vertebrae in CT images has significant importance in the auxiliary diagnosis and treatment of lumbar diseases. The U-Net architecture and its extended models have attracted extensive attention in the field of medical image segmentation. By addressing issues such as the loss of fine-grained features in lumbar segmentation using the Rolling U-Net model, an improved Rolling U-Net model is proposed for segmenting lumbar CT images. This model integrates a convolutional neural network(CNN) with a multi-layer perceptron(MLP). Feature excitation modules are inserted at the fourth convolutional layer and the bottleneck layer to increase the weight of key anatomical structures. By constructing long-range-local blocks(Lo2 blocks), it achieves the fusion of local feature information with long-range dependencies. The core R-MLP module within the Lo2 block learns long-range dependencies across the entire image in a single direction. By controlling and combining R-MLP modules oriented in different directions, OR-MLP and DOR-MLP modules are constructed to capture long-range dependencies in multiple directions. Finally, residual convolutions are integrated to restore segmentation details in the lumbar spine. Simultaneously, a MultiClassDiceCE loss function is designed by combining the pixel classification advantages of the Dice loss function and the cross-entropy loss function. Experimental results indicate that the number of categories and sampling strategies significantly impact the segmentation performance of the improved Rolling U-Net model. For binary segmentation tasks, the division strategy based on the total number of images is recommended to balance accuracy and stability, whereas the division strategy based on the total number of instances is better suited for multi-classification tasks. When performing multi-class segmentation tasks on the JST＿LV and VerSe datasets, the improved Rolling U-Net model outperformed segmentation models such as U-Net, Attention U-Net, and Rolling U-Net in terms of average Intersection over Union(IoU), Dice coefficient, recall, specificity, and precision. This demonstrates that the improved model effectively enhances the accuracy, integrity of detail, and classification robustness of lumbar spine CT image segmentation.更多还原