@inproceedings{95617c8d07864ac1b01b24ccaf88a799,
title = "FRNet: A Full-Resolution Convolutional Neural Network for OCTA Vascular Segmentation",
abstract = "Optical coherence tomography angiography (OCTA) is an advanced, non-invasive imaging technique. Automatic segmentation of vascular networks in retinal OCTA images is essential for the early diagnosis and progression assessment of various vision-related diseases. However, most existing methods for OCTA image vessel segmentation rely on encoder-decoder architectures, which typically involve a high number of parameters and result in slower inference speeds. In this paper, we introduce FRNet V2, an accurate and efficient neural network specifically designed for retinal vessel segmentation in OCTA images. This is achieved by integrating a modified recursive ConvNeXt V2 block and a recursive HAAM attention mechanism into a full-resolution convolutional network framework. We evaluate our proposed method on two large public datasets: ROSSA and OCTA-500. Experimental results demonstrate that our network achieves performance on par with other methods, while boasting significantly fewer parameters and faster inference speeds. This makes it highly suitable for practical industrial applications.",
keywords = "Optical coherence tomography angiography, Vascular segmentation, Neural networks, ConvNeXt V2, Dataset",
author = "Liang Wang and Dongxu Gao and Hongwei Gao and Guozhang Jiang and Zhaojie Ju",
year = "2024",
month = nov,
day = "12",
doi = "10.1109/M2VIP62491.2024.10746018",
language = "English",
isbn = "9798350391923",
series = "Proceedings of the International Conference on Mechatronics and Machine Vision in Practice (M2VIP)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 30th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)",
address = "United States",
note = "2024 30th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) ; Conference date: 03-10-2024 Through 05-10-2024",
}