Mr. Ziye Chen Profile

Ziye Chen

at Soochow Univ

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Publications (2)

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Proceedings Article | 9 October 2021 Poster + Presentation + Paper

Corneal nerve fiber segmentation and centerline extraction

Ziye Chen, Yunfeng Mo, Jiewen Chen, Jianhua Mo

Proceedings Volume 11900, 119001Q (2021) https://doi.org/10.1117/12.2600754

KEYWORDS: Image segmentation, Nerve, Image processing algorithms and systems, Diagnostics, Algorithm development, Feature extraction

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Corneal nerve fibers (CNF) usually exhibit changes associated with ocular and systemic diseases. Thus, in clinic, CNF is imaged by corneal confocal microscopy (CCM) for ocular disease diagnosis. To obtain an objective and accurate diagnosis, CNF needs to be segmented from CCM image and further its centerline is extracted to pave the wave for producing quantitative diagnostic markers. It is reasonable to state that the performance of CNF segmentation and centerline extraction places a big impact on the disease diagnosis. Therefore, in this study, we aim to improve the CNF segmentation and centerline extraction. CNF segmentation algorithm is developed based on UNet++, which is modified by adding skip connection to be more applicable to multi-scale information. Following CNF segmentation, a new CNF centerline extraction algorithm is developed based on neighborhood statistics. Compared with the traditional thinning algorithm and the skeleton extraction algorithm, our method yields a more consistent and smoother central line extraction by reducing the effect of imperfect segmentation and noise which is hard to avoid in deep learning segmentation. The proposed segmentation method is evaluated on an open dataset by comparing with the conventional UNet, UNet++ and UNet3+. The proposed centerline extraction method is evaluated on the same image dataset by comparing with the traditional thinning algorithm and the skeleton extraction algorithm. The results show that our method can outperform the conventional UNet++ in terms of Acc (accuracy), TPR (True Positive Rate), TNR (True Negative Rate), Dice and FDR (False Discovery Rate). The centerline extraction method can extract the centerline with less errors.

Proceedings Article | 9 October 2021 Poster + Presentation + Paper

Development of handheld optical coherent elastic imaging system

Yunfeng Mo, Ziye Chen, Jiewen Chen, Jianhua Mo

Proceedings Volume 11900, 119001P (2021) https://doi.org/10.1117/12.2600752

KEYWORDS: Sensors, Tissues, Optical coherence tomography, Tissue optics, Skin, Silicon, Imaging systems, Biomedical optics, Elastography, Polyurethane

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Optical coherence elastography (OCE) is a new biomedical optical elastic imaging technology. It inherits the advantage of optical coherence tomography (OCT) with high resolution, and it has sub-nanometer displacement measurement sensitivity. OCE uses OCT to detect the deformation of biological tissue along the depth direction under loading, so as to obtain the elastic information of tissue. Among the OCE forms with various loading strategies, compression OCE has attracted great interests for its ease of implementation. However, the quantitative measurement of the loading remains a challenge. Therefore, in this study, we developed a handheld OCE system based on compression OCE with a specially designed stress sensor for loading measurement. The OCE system is built based on swept-source OCT, and a handheld sampling probe was developed with a specially-designed pressure sensor for load measurement. The OCE probe with the stress sensor is evaluated on both artificial phantom and human skin. The results show that the OCE system has a good potential for elasticity measurement on biological tissues in vivo.

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