Qing Wu

Hangzhou Dianzi University

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Mingyu Xu

Institute of Automation

Pengyu Chen

Shenzhen Children's Hospital

Yiming Sun

Second Affiliated Hospital of Zhejiang University

Guiping Qian

Communication University of Zhejiang

Nuliqiman Maimaiti

Second Affiliated Hospital of Zhejiang University

Juan Ye

Second Affiliated Hospital of Zhejiang University

Wei Luo

Sichuan Agricultural University

Yan Wang

Yunnan Agricultural University

Jingxuan Cai

Nanjing University

Pengjie Chen

First Affiliated Hospital Zhejiang University

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Second Affiliated Hospital of Zhejiang University

Sichuan Agricultural University

Zhejiang University

Chinese Academy of Sciences

University of Science and Technology of China

University of Chinese Academy of Sciences

Sun Yat-sen University

Huazhong Agricultural University

Southwest Petroleum University

Hangzhou Dianzi University

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An AS-OCT image dataset for deep learning-enabled segmentation and 3D reconstruction for keratitis

Scientific Data (2024)

Yiming Sun Nuliqiman Maimaiti Peifang Xu Peng Jin Jingxuan Cai

Abstract Infectious keratitis is among the major causes of global blindness. Anterior segment optical coherence tomography (AS-OCT) images allow the characterizing of cross-sectional structures in the cornea with keratitis thus revealing the severity of inflammation, and can also provide 360-degree information on anterior chambers. The development of image analysis methods for such cases, particularly deep learning methods, requires a large number of annotated images, but to date, there is no such open-access AS-OCT image repository. For this reason, this work provides a dataset containing a total of 1168 AS-OCT images of patients with keratitis, including 768 full-frame images (6 patients). Each image has associated segmentation labels for lesions and cornea, and also labels of iris for full-frame images. This study provides a great opportunity to advance the field of image analysis on AS-OCT images in both two-dimensional (2D) and three-dimensional (3D) and would aid in the development of artificial intelligence-based keratitis management.

10.1038/s41597-024-03464-0

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SSREnricher: a computational approach for large-scale identification of polymorphic microsatellites based on comparative transcriptome analysis

PeerJ (2020)

Wei Luo Qing Wu Lan Yang Pengyu Chen Siqi Yang

Microsatellite (SSR) markers are the most popular markers for genetic analyses and molecular selective breeding in plants and animals. However, the currently available methods to develop SSRs are relatively time-consuming and expensive. One of the most factors is low frequency of polymorphic SSRs. In this study, we developed a software, SSREnricher, which composes of six core analysis procedures, including SSR mining, sequence clustering, sequence modification, enrichment containing polymorphic SSR sequences, false-positive removal and results output and multiple sequence alignment. After running of transcriptome sequences on this software, a mass of polymorphic SSRs can be identified. The validation experiments showed almost all markers (>90%) that were identified by the SSREnricher as putative polymorphic markers were indeed polymorphic. The frequency of polymorphic SSRs identified by SSREnricher was significantly higher ( P < 0.05) than that of traditional and HTS approaches. The software package is publicly accessible on GitHub ( https://github.com/byemaxx/SSREnricher ).

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10.7717/peerj.9372

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Citations (5)