SA2Net: Scale-adaptive structure-affinity transformation for spine segmentation from ultrasound volume projection imaging

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Hao Xie , Zixun Huang , Yushen Zuo , Yakun Ju , Frank H.F. Leung , N.F. Law , Kin-Man Lam , Yong-Ping Zheng , Sai Ho Ling
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引用次数: 0

Abstract

Spine segmentation, based on ultrasound volume projection imaging (VPI), plays a vital role for intelligent scoliosis diagnosis in clinical applications. However, this task faces several significant challenges. Firstly, the global contextual knowledge of spines may not be well-learned if we neglect the high spatial correlation of different bone features. Secondly, the spine bones contain rich structural knowledge regarding their shapes and positions, which deserves to be encoded into the segmentation process. To address these challenges, we propose a novel scale-adaptive structure-aware network (SA2Net) for effective spine segmentation. First, we propose a scale-adaptive complementary strategy to learn the cross-dimensional long-distance correlation features for spinal images. Second, motivated by the consistency between multi-head self-attention in Transformers and semantic level affinity, we propose structure-affinity transformation to transform semantic features with class-specific affinity and combine it with a Transformer decoder for structure-aware reasoning. In addition, we adopt a feature mixing loss aggregation method to enhance model training. This method improves the robustness and accuracy of the segmentation process. The experimental results demonstrate that our SA2Net achieves superior segmentation performance compared to other state-of-the-art methods. Moreover, the adaptability of SA2Net to various backbones enhances its potential as a promising tool for advanced scoliosis diagnosis using intelligent spinal image analysis.
基于尺度自适应结构亲和变换的超声体积投影成像脊柱分割。
基于超声体积投影成像(VPI)的脊柱分割对脊柱侧凸智能诊断具有重要的临床应用价值。然而,这项任务面临着几个重大挑战。首先,如果我们忽略了不同骨骼特征的高空间相关性,那么脊柱的全局上下文知识可能无法很好地学习。其次,脊柱骨骼包含丰富的形状和位置结构知识,值得编码到分割过程中。为了解决这些挑战,我们提出了一种新的规模自适应结构感知网络(SA2Net),用于有效的脊柱分割。首先,我们提出了一种尺度自适应互补策略来学习脊柱图像的跨维远距离相关特征。其次,基于Transformer中多头自注意与语义级亲和力的一致性,提出了结构-亲和力转换,将语义特征转换为类特定亲和力,并将其与Transformer解码器结合,实现结构感知推理。此外,我们采用特征混合损失聚合方法来增强模型训练。该方法提高了分割过程的鲁棒性和准确性。实验结果表明,与其他最先进的方法相比,我们的SA2Net实现了优越的分割性能。此外,SA2Net对各种脊柱的适应性增强了其作为智能脊柱图像分析高级脊柱侧凸诊断工具的潜力。
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来源期刊
CiteScore
10.70
自引率
3.50%
发文量
71
审稿时长
26 days
期刊介绍: The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.
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