冠状动脉疾病仍然是心血管疾病患者死亡的主要原因。通过支架植入的生物可吸收血管支架(BVS)的治疗用途很常见,然而,目前来自血管内光学相干断层扫描(IVOCT)图像的BVS分割技术的有效性不足.
■本文介绍了一种增强的分割方法,该方法使用一种新颖的基于小波的U形网络来解决这些挑战。我们开发了一种基于小波的U形网络,该网络结合了注意力门(AG)和Atrous多尺度场模块(AMFM),旨在通过改善支架支柱与周围组织之间的差异来提高分割精度。独特的小波融合模块缓解了不同特征图分支之间的语义差距,促进更有效的功能集成。
■大量实验表明,我们的模型在关键指标如Dice系数方面超越了现有技术,准确度,灵敏度,和联合交汇处(IoU),得分达到85.10%,99.77%,86.93%,73.81%,分别。AG的整合,AMFM,融合模块在实现这些结果方面发挥了关键作用,指示在捕获详细的上下文信息方面的显著增强。
■基于小波的U形网络的引入标志着IVOCT图像中BVSs分割的实质性改进,提示冠心病治疗临床实践的潜在益处。这种方法也可能适用于其他复杂的医学成像分割任务,表明未来研究的广阔范围。
UNASSIGNED: Coronary artery disease remains a leading cause of mortality among individuals with cardiovascular conditions. The therapeutic use of bioresorbable vascular scaffolds (BVSs) through stent implantation is common, yet the effectiveness of current BVS segmentation techniques from Intravascular Optical Coherence Tomography (IVOCT) images is inadequate.
UNASSIGNED: This paper introduces an enhanced segmentation approach using a novel Wavelet-based U-shape network to address these challenges. We developed a Wavelet-based U-shape network that incorporates an Attention Gate (AG) and an Atrous Multi-scale Field Module (AMFM), designed to enhance the segmentation accuracy by improving the differentiation between the stent struts and the surrounding tissue. A unique wavelet fusion module mitigates the semantic gaps between different feature map branches, facilitating more effective feature integration.
UNASSIGNED: Extensive experiments demonstrate that our model surpasses existing techniques in key metrics such as Dice coefficient, accuracy, sensitivity, and Intersection over Union (IoU), achieving scores of 85.10%, 99.77%, 86.93%, and 73.81%, respectively. The integration of AG, AMFM, and the fusion module played a crucial role in achieving these outcomes, indicating a significant enhancement in capturing detailed contextual information.
UNASSIGNED: The introduction of the Wavelet-based U-shape network marks a substantial improvement in the segmentation of BVSs in IVOCT images, suggesting potential benefits for clinical practices in coronary artery disease treatment. This approach may also be applicable to other intricate medical imaging segmentation tasks, indicating a broad scope for future research.