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Your search keyword '"Nuliqiman Maimaiti"' showing total 5 results
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5 results on '"Nuliqiman Maimaiti"'

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

Author

Yiming Sun, Nuliqiman Maimaiti, Peifang Xu, Peng Jin, Jingxuan Cai, Guiping Qian, Pengjie Chen, Mingyu Xu, Gangyong Jia, Qing Wu, and Juan Ye

Subjects
Science
Abstract

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.

Published
2024
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2. LPUWF-LDM: Enhanced Latent Diffusion Model for Precise Late-phase UWF-FA Generation on Limited Dataset

Author

Fang, Zhaojie, Yu, Xiao, Zhou, Guanyu, Zhuang, Ke, Chen, Yifei, Ge, Ruiquan, Wang, Changmiao, Jia, Gangyong, Wu, Qing, Ye, Juan, Nuliqiman, Maimaiti, Xu, Peifang, and Elazab, Ahmed

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Ultra-Wide-Field Fluorescein Angiography (UWF-FA) enables precise identification of ocular diseases using sodium fluorescein, which can be potentially harmful. Existing research has developed methods to generate UWF-FA from Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) to reduce the adverse reactions associated with injections. However, these methods have been less effective in producing high-quality late-phase UWF-FA, particularly in lesion areas and fine details. Two primary challenges hinder the generation of high-quality late-phase UWF-FA: the scarcity of paired UWF-SLO and early/late-phase UWF-FA datasets, and the need for realistic generation at lesion sites and potential blood leakage regions. This study introduces an improved latent diffusion model framework to generate high-quality late-phase UWF-FA from limited paired UWF images. To address the challenges as mentioned earlier, our approach employs a module utilizing Cross-temporal Regional Difference Loss, which encourages the model to focus on the differences between early and late phases. Additionally, we introduce a low-frequency enhanced noise strategy in the diffusion forward process to improve the realism of medical images. To further enhance the mapping capability of the variational autoencoder module, especially with limited datasets, we implement a Gated Convolutional Encoder to extract additional information from conditional images. Our Latent Diffusion Model for Ultra-Wide-Field Late-Phase Fluorescein Angiography (LPUWF-LDM) effectively reconstructs fine details in late-phase UWF-FA and achieves state-of-the-art results compared to other existing methods when working with limited datasets. Our source code is available at: https://github.com/Tinysqua/****., Comment: 13 pages, 7 figures

Published
2024

5. Artificial Intelligence in Ophthalmic Surgery: Current Applications and Expectations

Author

Nuliqiman,Maimaiti, Xu,Mingyu, Sun,Yiming, Cao,Jing, Chen,Pengjie, Gao,Qi, Xu,Peifang, Ye,Juan, Nuliqiman,Maimaiti, Xu,Mingyu, Sun,Yiming, Cao,Jing, Chen,Pengjie, Gao,Qi, Xu,Peifang, and Ye,Juan

Abstract

Maimaiti Nuliqiman, Mingyu Xu, Yiming Sun, Jing Cao, Pengjie Chen, Qi Gao, Peifang Xu, Juan Ye Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, People’s Republic of ChinaCorrespondence: Peifang Xu; Juan Ye, Tel +86-571-8778-3897, Fax +86-571-8706-8001, Email xpf1900@zju.edu.cn; yejuan@zju.edu.cnAbstract: Artificial Intelligence (AI) has found rapidly growing applications in ophthalmology, achieving robust recognition and classification in most kind of ocular diseases. Ophthalmic surgery is one of the most delicate microsurgery, requiring high fineness and stability of surgeons. The massive demand of the AI assist ophthalmic surgery will constitute an important factor in boosting accelerate precision medicine. In clinical practice, it is instrumental to update and review the considerable evidence of the current AI technologies utilized in the investigation of ophthalmic surgery involved in both the progression and innovation of precision medicine. Bibliographic databases including PubMed and Google Scholar were searched using keywords such as “ophthalmic surgery”, “surgical selection”, “candidate screening”, and “robot-assisted surgery” to find articles about AI technology published from 2018 to 2023. In addition to the Editorials and letters to the editor, all types of approaches are considered. In this paper, we will provide an up-to-date review of artificial intelligence in eye surgery, with a specific focus on its application to candidate screening, surgery selection, postoperative prediction, and real-time intraoperative guidance.Keywords: ophthalmic surgery, AI, machine learning, surgery selection, candidate screening, robot-assisted surgery

Published
2023

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