One-Shot Learning for Optical Coherence Tomography Angiography Vessel Segmentation Based on Multi-Scale U 2 -Net

Shudong Liu, Shuai Guo, Jia Cong, Yue Yang, Zihui Guo and Boyu Gu ()
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Shudong Liu: School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China
Shuai Guo: School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China
Jia Cong: School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China
Yue Yang: School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China
Zihui Guo: School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China
Boyu Gu: School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China

Mathematics, 2023, vol. 11, issue 24, 1-16

Abstract: Vessel segmentation in optical coherence tomography angiography (OCTA) is crucial for the detection and diagnosis of various eye diseases. However, it is hard to distinguish intricate vessel morphology and quantify the density of blood vessels due to the large variety of vessel sizes, significant background noise, and small datasets. To this end, a retinal angiography multi-scale segmentation network, integrated with the inception and squeeze-and-excitation modules, is proposed to address the above challenges under the one-shot learning paradigm. Specifically, the inception module extends the receptive field and extracts multi-scale features effectively to handle diverse vessel sizes. Meanwhile, the squeeze-and-excitation module modifies channel weights adaptively to improve the vessel feature extraction ability in complex noise backgrounds. Furthermore, the one-shot learning paradigm is adapted to alleviate the problem of the limited number of images in existing retinal OCTA vascular datasets. Compared with the classic U 2 -Net, the proposed model gains improvements in the Dice coefficient, accuracy, precision, recall, and intersection over union by 3.74%, 4.72%, 8.62%, 4.87%, and 4.32% respectively. The experimental results demonstrate that the proposed one-shot learning method is an effective solution for retinal angiography image segmentation.

Keywords: optical coherence tomography angiography; retinal angiography multi-scale segmentation; one-shot learning; image processing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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