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Received:June 05, 2024 Revised:June 28, 2024
Received:June 05, 2024 Revised:June 28, 2024
中文摘要: 骶髂关节病变是预警强直性脊柱炎的主要体征之一, 精确高效的骶髂关节自动分割对于协助医生临床诊断和治疗至关重要. 针对骶髂关节灰度多变、背景复杂、且因骶髂间隙狭小而存在容积效应导致的特征提取受限, 分割精度难以提升的问题, 本研究利用层次级联补偿下采样信息丢失以及注意力并行保留跨维信息特征的思想, 提出首个用于骶髂关节分割诊断的U型网络. 此外, 为了提高临床诊断的效率, 将U型网络中传统的卷积替换为高效部分卷积块. 本实验在山西白求恩医院提供的骶髂关节CT数据集中, 验证了分割精度及效率平衡方面的有效性, 最终DICE达到91.52%, IoU达到84.41%. 实验结果表明, 改进的U型分割网络能有效提高骶髂关节分割精度, 减轻医疗专业人员的负担.
Abstract:A lesion of the sacroiliac joint is one of the primary signs for the early warning of ankylosing spondylitis. Accurate and efficient automatic segmentation of the sacroiliac joint is crucial for assisting doctors in clinical diagnosis and treatment. The limitations in feature extraction in sacroiliac joint CT images, due to diverse gray levels, complex backgrounds, and volume effects resulting from the narrow sacroiliac joint gap, hinder the improvement of segmentation accuracy. To address these problems, this study proposes the first U-shaped network for sacroiliac joint segmentation diagnosis, utilizing the concept of hierarchical cascade compensation for downsampling information loss and parallel attention preservation of cross-dimensional information features. Moreover, to enhance the efficiency of clinical diagnosis, the traditional convolutions in the U-shaped network are replaced with efficient partial convolution blocks. The experiment, conducted on a sacroiliac joint CT dataset provided by Shanxi Bethune Hospital, validates the effectiveness of the proposed network in balancing segmentation accuracy and efficiency. The network achieves a DICE value of 91.52% and an IoU of 84.41%. The results indicate that the improved U-shaped segmentation network effectively enhances the accuracy of sacroiliac joint segmentation and reduces the workload of medical professionals.
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严武军,王家辉,邱瑜茹.PAF-Net: 用于骶髂关节高效分割的并行注意力网络.计算机系统应用,,():1-8
YAN Wu-Jun,WANG Jia-Hui,QIU Yu-Ru.PAF-Net: Parallel Attention Network for Efficient Sacroiliac Joint Segmentation.COMPUTER SYSTEMS APPLICATIONS,,():1-8
严武军,王家辉,邱瑜茹.PAF-Net: 用于骶髂关节高效分割的并行注意力网络.计算机系统应用,,():1-8
YAN Wu-Jun,WANG Jia-Hui,QIU Yu-Ru.PAF-Net: Parallel Attention Network for Efficient Sacroiliac Joint Segmentation.COMPUTER SYSTEMS APPLICATIONS,,():1-8