Huang Yan, Yang Jinzhu, Sun Qi, Yuan Yuliang, Hou Yang, Shang Jin
Key Laboratory of Intelligent Computing in Medical image, Ministry of Education, Northeastern University, Shenyang, 110819, China.
School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China.
Med Biol Eng Comput. 2025 May 13. doi: 10.1007/s11517-025-03368-0.
Accurate segmentation of small vessels, such as coronary and pulmonary arteries, is crucial for early detection and treatment of vascular diseases. However, challenges persist due to the vessel's small size, complex structures, morphological variations, and limited annotated data. To address these challenges, we propose a detail-preserving network enhanced by a discriminator to improve the few-shot small vessel segmentation performance. The detail-preserving network constructs a complex module with multi-residual hybrid dilated convolution, which can enhance the network's receptive field while preserving the image's full detail features, enabling it to better capture the small vessel's structural features. Simultaneously, discriminator enhancement is incorporated into the training process through adversarial learning, effectively utilizing large amounts of unlabeled data to boost the generalization and robustness of the segmentation model. We validate the proposed method on in-house and public coronary artery datasets and public pulmonary artery datasets. Experimental results demonstrate that the proposed method significantly improves segmentation accuracy, particularly for small vessels. Compared with other state-of-the-art methods, the proposed method achieves higher accuracy, a lower false positive rate, and superior generalization capability, effectively assisting the clinical diagnosis of vessel diseases.
准确分割诸如冠状动脉和肺动脉等小血管，对于血管疾病的早期检测和治疗至关重要。然而，由于血管尺寸小、结构复杂、形态变异以及标注数据有限，挑战依然存在。为应对这些挑战，我们提出一种由鉴别器增强的细节保留网络，以提高少样本小血管分割性能。细节保留网络构建了一个具有多残差混合扩张卷积的复杂模块，该模块可以在保留图像完整细节特征的同时增强网络的感受野，使其能够更好地捕捉小血管的结构特征。同时，通过对抗学习将鉴别器增强纳入训练过程，有效利用大量未标注数据来提升分割模型的泛化能力和鲁棒性。我们在内部和公共冠状动脉数据集以及公共肺动脉数据集上验证了所提出的方法。实验结果表明，所提出的方法显著提高了分割精度，特别是对于小血管。与其他现有最先进方法相比，所提出的方法实现了更高的精度、更低的假阳性率和更优的泛化能力，有效辅助血管疾病的临床诊断。
