Faqiang Shi

Beihang University

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Shanshan Wei

Jilin Agricultural University

Zhi Cai

China Electric Power Research Institute

Jianwei Niu

Beihang University

Yilin Chou

Nanjing Medical University

Xiaozheng Xie

Lanzhou University of Technology

Yunchao Gu

Huzhou Central Hospital

Xiaolan Tang

Chongqing Medical University

Yuexin Wang

Peking University

Xutan Peng

University of Sheffield

Chen Li

Dalian University

Cooperative Institutions

Beihang University

Peking University

Peking University Third Hospital

Jilin Agricultural University

Shandong First Medical University

State Key Laboratory of Virtual Reality Technology and Systems

Chongqing Medical University

Tianjin Medical University

Yunnan University of Traditional Chinese Medicine

Guilin University of Electronic Technology

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A Deep Learning Model for Automated Sub-Basal Corneal Nerve Segmentation and Evaluation Using In Vivo Confocal Microscopy

Translational Vision Science & Technology (2020)

Shanshan Wei Faqiang Shi Yuexin Wang Yilin Chou Xuemin Li

Purpose: The purpose of this study was to establish a deep learning model for automated sub-basal corneal nerve fiber (CNF) segmentation and evaluation with in vivo confocal microscopy (IVCM). Methods: A corneal nerve segmentation network (CNS-Net) was established with convolutional neural networks based on a deep learning algorithm for sub-basal corneal nerve segmentation and evaluation. CNS-Net was trained with 552 and tested on 139 labeled IVCM images as supervision information collected from July 2017 to December 2018 in Peking University Third Hospital. These images were labeled by three senior ophthalmologists with ImageJ software and then considered ground truth. The areas under the receiver operating characteristic curves (AUCs), mean average precision (mAP), sensitivity, and specificity were applied to evaluate the efficiency of corneal nerve segmentation. The relative deviation ratio (RDR) was leveraged to evaluate the accuracy of the corneal nerve fiber length (CNFL) evaluation task. Results: The model achieved an AUC of 0.96 (95% confidence interval [CI] = 0.935–0.983) and an mAP of 94% with minimum dice coefficient loss at 0.12. For our dataset, the sensitivity was 96% and specificity was 75% in the CNF segmentation task, and an RDR of 16% was reported in the CNFL evaluation task. Moreover, the model was able to segment and evaluate as many as 32 images per second, much faster than skilled ophthalmologists. Conclusions: We established a deep learning model, CNS-Net, which demonstrated a high accuracy and fast speed in sub-basal corneal nerve segmentation with IVCM. The results highlight the potential of the system in assisting clinical practice for corneal nerves segmentation and evaluation. Translational Relevance: The deep learning model for IVCM images may enable rapid segmentation and evaluation of the corneal nerve and may provide the basis for the diagnosis and treatment of ocular surface diseases associated with corneal nerves.

Nerve fiber

Ground truth

Basal (medicine)

10.1167/tvst.9.2.32

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Citations (40)

A Lightweight Music Texture Transfer System

arXiv (Cornell University) (2018)

Xutan Peng Chen Li Zhi Cai Faqiang Shi Yidan Liu

Deep learning researches on the transformation problems for image and text have raised great attention. However, present methods for music feature transfer using neural networks are far from practical application. In this paper, we initiate a novel system for transferring the texture of music, and release it as an open source project. Its core algorithm is composed of a converter which represents sounds as texture spectra, a corresponding reconstructor and a feed-forward transfer network. We evaluate this system from multiple perspectives, and experimental results reveal that it achieves convincing results in both sound effects and computational performance.

Texture (cosmology)

10.48550/arxiv.1810.01248

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Breast Ultrasound Image Classification and Segmentation Using Convolutional Neural Networks

Lecture notes in computer science (2018)

Xiaozheng Xie Faqiang Shi Jianwei Niu Xiaolan Tang

Breast ultrasound

10.1007/978-3-030-00764-5_19

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Citations (37)

Data Acquisition and Processing of Breast Cancer Assisted Diagnosis Based on Ultrasound Imaging

Yunchao Gu Faqiang Shi

Breast disease is a common disease in women. The analysis and judgment of B-mode ultrasound images by doctors depend heavily on the operation experience and technical level of doctors. Computer image processing technologies such as natural image classification, target detection and semantic segmentation, represented by deep learning, have been relatively mature, and have been widely used successfully in automatic driving, security, finance and other fields. In this paper, through consultation and cooperation with medical institutions, a large mammary ultrasound image data set is constructed, which basically meets the needs of deep neural network training and validation testing. It is used to develop and validate algorithms for subsequent sub-tasks of ultrasound image analysis.

Breast ultrasound

Data set

10.1145/3322134.3322148

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Citations (1)

Design of Coal Mine Communication System Based on WiFi Technology

Mining Safety & Environmental Protection (2013)

Faqiang Shi

A coal mine communication system based on WiFi technology was designed according to the needs of safe production in the mine.The underground facilities of the system include the personnel positioning system,wireless communication,video collection,voice call,environmental parameter monitoring and substation,its ground facilities include the host,switches,servers,gateways,etc.The system took the wireless WiFi AP as the nodes and can realize the integration of underground positioning,VOIP voice,wireless mobile digital video,sensors monitoring and so on.The test results showed that the system has the features of simple structure,stable operation,strong anti-interference ability and high reliability.

Voice communication

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