Zhang Zhiqing, Liu Tianyong, Fan Guojia, Pu Yao, Li Bin, Chen Xingyu, Feng Qianjin, Zhou Shoujun
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Bioengineering (Basel). 2024 Oct 15;11(10):1031. doi: 10.3390/bioengineering11101031.
Spinal medical image segmentation is critical for diagnosing and treating spinal disorders. However, ambiguity in anatomical boundaries and interfering factors in medical images often cause segmentation errors. Current deep learning models cannot fully capture the intrinsic data properties, leading to unstable feature spaces. To tackle the above problems, we propose Verdiff-Net, a novel diffusion-based segmentation framework designed to improve segmentation accuracy and stability by learning the underlying data distribution. Verdiff-Net integrates a multi-scale fusion module (MSFM) for fine feature extraction and a noise semantic adapter (NSA) to refine segmentation masks. Validated across four multi-modality spinal datasets, Verdiff-Net achieves a high Dice coefficient of 93%, demonstrating its potential for clinical applications in precision spinal surgery.
脊柱医学图像分割对于脊柱疾病的诊断和治疗至关重要。然而，解剖边界的模糊性以及医学图像中的干扰因素常常导致分割错误。当前的深度学习模型无法完全捕捉内在的数据属性，从而导致特征空间不稳定。为了解决上述问题，我们提出了Verdiff-Net，这是一种基于扩散的新型分割框架，旨在通过学习潜在的数据分布来提高分割精度和稳定性。Verdiff-Net集成了用于精细特征提取的多尺度融合模块（MSFM）和用于细化分割掩码的噪声语义适配器（NSA）。在四个多模态脊柱数据集上得到验证，Verdiff-Net实现了93%的高骰子系数，证明了其在精准脊柱手术临床应用中的潜力。
