通过融合边界特征提升牙齿图像分割精度
Boundary feature fusion network for tooth image segmentation
- 设计边界特征提取模块，从高层特征中捕捉精细边界信息
- 提出特征交叉融合机制，协同传递边界与语义信息
- 在STS数据挑战赛中取得0.91的高分，适合牙科影像分析
牙齿分割是医学图像分割领域的重要技术，广泛应用于正畸治疗、人体识别及牙科病理评估。尽管研究者提出了多种牙齿图像分割模型，但普遍存在未充分应对牙齿边界模糊的问题。精准的牙齿边界划分对牙科诊断至关重要。本文提出一种创新的牙齿分割网络，通过整合边界信息解决牙齿与邻近组织间边界不清的难题。该网络的核心为边界特征提取模块，可从高层特征中提取详细的边界信息；同时，特征交叉融合模块以协同方式融合边界细节与全局语义信息，实现特征的逐层传递。该方法显著提升了分割精度。在最新的STS数据挑战赛中，本方法获得0.91的综合评分，相较于现有方法表现更优。
原文摘要 · Abstract (English)
Tooth segmentation is a critical technology in the field of medical image segmentation, with applications ranging from orthodontic treatment to human body identification and dental pathology assessment. Despite the development of numerous tooth image segmentation models by researchers, a common shortcoming is the failure to account for the challenges of blurred tooth boundaries. Dental diagnostics require precise delineation of tooth boundaries. This paper introduces an innovative tooth segmentation network that integrates boundary information to address the issue of indistinct boundaries between teeth and adjacent tissues. This network's core is its boundary feature extraction module, which is designed to extract detailed boundary information from high-level features. Concurrently, the feature cross-fusion module merges detailed boundary and global semantic information in a synergistic way, allowing for stepwise layer transfer of feature information. This method results in precise tooth segmentation. In the most recent STS Data Challenge, our methodology was rigorously tested and received a commendable overall score of 0.91. When compared to other existing approaches, this score demonstrates our method's significant superiority in segmenting tooth boundaries.
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