Robust vascular segmentation for raw complex images of laser speckle contrast based on weakly supervised learning

• Published in: IEEE transactions on medical imaging 2024-01, Vol.43 (1), p.1-1
• Main Authors: Fu, Suzhong, Xu, Jing, Chang, Shilong, Yang, Luyao, Ling, Shuting, Cai, Jinghan, Chen, Jiayin, Yuan, Jiacheng, Cai, Ying, Zhang, Bei, Huang, Zicheng, Yang, Kun, Sui, Wenhai, Xue, Linyan, Zhao, Qingliang
• Format: Article
• Language: English
• Subjects: Annotations; Artificial Intelligence; Artificial neural networks; Biomedical imaging; Blood flow; Complexity; convolutional neural network (CNN); Datasets; Deep learning; Embolization; Image contrast; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Imaging; laser speckle contrast imaging (LSCI); Lasers; Machine learning; Medical imaging; Microcirculation - physiology; Neural networks; Neural Networks, Computer; Robustness; Segmentation; Speckle; Supervised learning; Supervised Machine Learning; Teaching methods; Temporal resolution; vascular segmentation; weakly supervised learning
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2023.3287200

Laser speckle contrast imaging (LSCI) is widely used for in vivo real-time detection and analysis of local blood flow microcirculation due to its non-invasive ability and excellent spatial and temporal resolution. However, vascular segmentation of LSCI images still faces a lot of difficulties due to numerous specific noises caused by the complexity of blood microcirculation's structure and irregular vascular aberrations in diseased regions. In addition, the difficulties of LSCI image data annotation have hindered the application of deep learning methods based on supervised learning in the field of LSCI image vascular segmentation. To tackle these difficulties, we propose a robust weakly supervised learning method, which selects the threshold combinations and processing flows instead of labor-intensive annotation work to construct the ground truth of the dataset, and design a deep neural network, FURNet, based on UNet++ and ResNeXt. The model obtained from training achieves high-quality vascular segmentation and captures multi-scene vascular features on both constructed and unknown datasets with good generalization. Furthermore, we intravital verified the availability of this method on a tumor before and after embolization treatment. This work provides a new approach for realizing LSCI vascular segmentation and also makes a new application-level advance in the field of artificial intelligence-assisted disease diagnosis.