Mr. Jiaxuan Li Profile

Jiaxuan Li

at Shanghai Jiao Tong Univ

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Proceedings Article | 4 April 2022 Presentation + Paper

Multi-scale sparse representation-based shadow inpainting for retinal OCT images

Yaoqi Tang, Yufan Li, Hongshan Liu, Jiaxuan Li, Peiyao Jin, Yu Gan, Yuye Ling, Yikai Su

Proceedings Volume 12032, 1203204 (2022) https://doi.org/10.1117/12.2611281

KEYWORDS: Optical coherence tomography, Image segmentation, Image processing, Super resolution, Medical imaging

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Inpainting shadowed regions cast by superficial blood vessels in retinal optical coherence tomography (OCT) images is critical for accurate and robust machine analysis and clinical diagnosis. Traditional sequence-based approaches such as propagating neighboring information to gradually fill in the missing regions are cost-effective. But they generate less satisfactory outcomes when dealing with larger missing regions and texture-rich structures. Emerging deep learning-based methods such as encoder-decoder networks have shown promising results in natural image inpainting tasks. However, they typically need a long computational time for network training in addition to the high demand on the size of datasets, which makes it difficult to be applied on often small medical datasets. To address these challenges, we propose a novel multi-scale shadow inpainting framework for OCT images by synergically applying sparse representation and deep learning: sparse representation is used to extract features from a small amount of training images for further inpainting and to regularize the image after the multi-scale image fusion, while convolutional neural network (CNN) is employed to enhance the image quality. During the image inpainting, we divide preprocessed input images into different branches based on the shadow width to harvest complementary information from different scales. Finally, a sparse representation-based regularizing module is designed to refine the generated contents after multi-scale feature aggregation. Experiments are conducted to compare our proposal versus both traditional and deep learning-based techniques on synthetic and real-world shadows. Results demonstrate that our proposed method achieves favorable image inpainting in terms of visual quality and quantitative metrics, especially when wide shadows are presented.

Proceedings Article | 5 March 2021 Presentation + Paper

A GCN-assisted deep learning method for peripapillary retinal layer segmentation in OCT images

Jiaxuan Li, Yuye Ling, Jiangnan He, Peiyao Jin, Jianfeng Zhu, Haidong Zou, Xun Xu, Shuo Shao, Yu Gan, Yikai Su

Proceedings Volume 11630, 1163010 (2021) https://doi.org/10.1117/12.2582905

KEYWORDS: Image segmentation, Optical coherence tomography, Neural networks, Retina, Nerve, Computer programming

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Accurate retinal layer segmentation, especially the peripapillary retinal nerve fiber layer (RNFL) is critical for the diagnosis of ophthalmic diseases. However, due to the complex morphologies of the peripapillary region, most of the existing methods focus on segmenting the macular region and could not be directly applied to the peripapillary retinal optical coherence tomography (OCT) images. In this paper, we propose a novel graph convolutional network (GCN)-assisted segmentation framework based on a U-shape neural network for peripapillary retinal layer segmentation in OCT images. We argue that the strictly stratified structure of retina layers in addition to the centered optic disc is an ideal objective for GCN. Specifically, a graph reasoning block is inserted between the encoder and decoder of the U-shape neural network to conduct spatial reasoning. In this way, the peripapillary retina in OCT images is segmented into nine layers including RNFL. The proposed method was trained and tested on our collected dataset of peripapillary retinal OCT images. Experimental results showed that our segmentation method outperformed other state-of-the-art methods. In particular, compared with ReLayNet, the average and RNFL Dice coefficients are improved by 1.2% and 2.6%, respectively.

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