Tingwei Quan

Wuhan National Laboratory for Optoelectronics

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Hui Gong

Huazhong University of Science and Technology

Shaoqun Zeng

Huazhong University of Science and Technology

Hang Zhou

Huazhong University of Science and Technology

Qingming Luo

Huazhong University of Science and Technology

Shiwei Li

Ningbo University

Jing Li

Huazhong University of Science and Technology

Ting Zheng

Twelfth Guangzhou City People's Hospital

Zhongqing Yang

Gansu Agricultural University

Zehua Wang

Hangzhou City University

Feng Xi

Nanjing University of Science and Technology

Cooperative Institutions

Huazhong University of Science and Technology

Wuhan National Laboratory for Optoelectronics

Chinese PLA General Hospital

Suzhou Research Institute

Tongji Hospital

Jiangsu Industry Technology Research Institute

Ministry of Industry and Information Technology

Harbin Institute of Technology

Chinese Academy of Medical Sciences & Peking Union Medical College

Chinese People's Liberation Army

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Robust whole slide image analysis for cervical cancer screening using deep learning

Nature Communications (2021)

Shenghua Cheng Sibo Liu Jingya Yu Gong Rao Yuwei Xiao

Computer-assisted diagnosis is key for scaling up cervical cancer screening. However, current recognition algorithms perform poorly on whole slide image (WSI) analysis, fail to generalize for diverse staining and imaging, and show sub-optimal clinical-level verification. Here, we develop a progressive lesion cell recognition method combining low- and high-resolution WSIs to recommend lesion cells and a recurrent neural network-based WSI classification model to evaluate the lesion degree of WSIs. We train and validate our WSI analysis system on 3,545 patient-wise WSIs with 79,911 annotations from multiple hospitals and several imaging instruments. On multi-center independent test sets of 1,170 patient-wise WSIs, we achieve 93.5% Specificity and 95.1% Sensitivity for classifying slides, comparing favourably to the average performance of three independent cytopathologists, and obtain 88.5% true positive rate for highlighting the top 10 lesion cells on 447 positive slides. After deployment, our system recognizes a one giga-pixel WSI in about 1.5 min.

10.1038/s41467-021-25296-x

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Citations (109)

A Clinical Bacterial Dataset for Deep Learning in Microbiological Rapid On-Site Evaluation

Scientific Data (2024)

X Y Wang Yinghan Shi Sha‐Sha Guo Xuzhong Qu Fei Xie

Microbiological Rapid On-Site Evaluation (M-ROSE) is based on smear staining and microscopic observation, providing critical references for the diagnosis and treatment of pulmonary infectious disease. Automatic identification of pathogens is the key to improving the quality and speed of M-ROSE. Recent advancements in deep learning have yielded numerous identification algorithms and datasets. However, most studies focus on artificially cultured bacteria and lack clinical data and algorithms. Therefore, we collected Gram-stained bacteria images from lower respiratory tract specimens of patients with lung infections in Chinese PLA General Hospital obtained by M-ROSE from 2018 to 2022 and desensitized images to produce 1705 images (4,912 × 3,684 pixels). A total of 4,833 cocci and 6,991 bacilli were manually labelled and differentiated into negative and positive. In addition, we applied the detection and segmentation networks for benchmark testing. Data and benchmark algorithms we provided that may benefit the study of automated bacterial identification in clinical specimens.

10.1038/s41597-024-03370-5

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Citations (1)

NeuroGPS: automated localization of neurons for brain circuits using L1 minimization model

Scientific Reports (2013)

Tingwei Quan Ting Zheng Zhongqing Yang Wenxiang Ding Shiwei Li

Drawing the map of neuronal circuits at microscopic resolution is important to explain how brain works. Recent progresses in fluorescence labeling and imaging techniques have enabled measuring the whole brain of a rodent like a mouse at submicron-resolution. Considering the huge volume of such datasets, automatic tracing and reconstruct the neuronal connections from the image stacks is essential to form the large scale circuits. However, the first step among which, automated location the soma across different brain areas remains a challenge. Here, we addressed this problem by introducing L1 minimization model. We developed a fully automated system, NeuronGlobalPositionSystem (NeuroGPS) that is robust to the broad diversity of shape, size and density of the neurons in a mouse brain. This method allows locating the neurons across different brain areas without human intervention. We believe this method would facilitate the analysis of the neuronal circuits for brain function and disease studies.

Tracing

Neuronal Circuits

Biological neural network

Brain Function

Minification

10.1038/srep01414

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Citations (62)

Modelling brain-wide neuronal morphology via rooted Cayley trees

Scientific Reports (2018)

Congping Lin Yuanfei Huang Tingwei Quan Yiwei Zhang

Abstract Neuronal morphology is an essential element for brain activity and function. We take advantage of current availability of brain-wide neuron digital reconstructions of the Pyramidal cells from a mouse brain, and analyze several emergent features of brain-wide neuronal morphology. We observe that axonal trees are self-affine while dendritic trees are self-similar. We also show that tree size appear to be random, independent of the number of dendrites within single neurons. Moreover, we consider inhomogeneous branching model which stochastically generates rooted 3-Cayley trees for the brain-wide neuron topology. Based on estimated order-dependent branching probability from actual axonal and dendritic trees, our inhomogeneous model quantitatively captures a number of topological features including size and shape of both axons and dendrites. This sheds lights on a universal mechanism behind the topological formation of brain-wide axonal and dendritic trees.

Branching (polymer chemistry)

Morphology

Tree (set theory)

10.1038/s41598-018-34050-1

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Citations (12)

A platform for stereological quantitative analysis of the brain-wide distribution of type-specific neurons

Scientific Reports (2017)

Chen Zhang Cheng Yan Miao Ren Anan Li Tingwei Quan

Quantifying the distribution of specific neurons throughout the whole brain is crucial for understanding physiological actions, pathological alterations and pharmacological treatments. However, the precise cell number and density of specific neurons in the entire brain remain unknown because of a lack of suitable research tools. Here, we propose a pipeline to automatically acquire and analyse the brain-wide distribution of type-specific neurons in a mouse brain. We employed a Brain-wide Positioning System to collect high-throughput anatomical information with the co-localized cytoarchitecture of the whole brain at subcellular resolution and utilized the NeuroGPS algorithm to locate and count cells in the whole brain. We evaluated the data continuity of the 3D dataset and the accuracy of stereological cell counting in 3D. To apply this pipeline, we acquired and quantified the brain-wide distributions and somatic morphology of somatostatin-expressing neurons in transgenic mouse brains. The results indicated that this whole-brain cell counting pipeline has the potential to become a routine tool for cell type neuroscience studies.

Cytoarchitecture

Cell type

Brain atlas

Brain morphometry

10.1038/s41598-017-14699-w

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Citations (30)

Digital reconstruction of the cell body in dense neural circuits using a spherical-coordinated variational model

Scientific Reports (2014)

Tingwei Quan Jing Li Hang Zhou Shiwei Li Ting Zheng

Mapping the neuronal circuits is essential to understand brain function. Recent technological advancements have made it possible to acquire the brain atlas at single cell resolution. Digital reconstruction of the neural circuits down to this level across the whole brain would significantly facilitate brain studies. However, automatic reconstruction of the dense neural connections from microscopic image still remains a challenge. Here we developed a spherical-coordinate based variational model to reconstruct the shape of the cell body i.e. soma, as one of the procedures for this purpose. When intuitively processing the volumetric images in the spherical coordinate system, the reconstruction of somas with variational model is no longer sensitive to the interference of the complicated neuronal morphology and could automatically and robustly achieve accurate soma shape regardless of the dense spatial distribution and diversity in cell size and morphology. We believe this method would speed drawing the neural circuits and boost brain studies.

Biological neural network

10.1038/srep04970

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Citations (13)

Reconstruction of micron resolution mouse brain surface from large-scale imaging dataset using resampling-based variational model

Scientific Reports (2015)

Jing Li Tingwei Quan Shiwei Li Hang Zhou Qingming Luo

Brain surface profile is essential for brain studies, including registration, segmentation of brain structure and drawing neuronal circuits. Recent advances in high-throughput imaging techniques enable imaging whole mouse brain at micron spatial resolution and provide a basis for more fine quantitative studies in neuroscience. However, reconstructing micron resolution brain surface from newly produced neuronal dataset still faces challenges. Most current methods apply global analysis, which are neither applicable to a large imaging dataset nor to a brain surface with an inhomogeneous signal intensity. Here, we proposed a resampling-based variational model for this purpose. In this model, the movement directions of the initial boundary elements are fixed, the final positions of the initial boundary elements that form the brain surface are determined by the local signal intensity. These features assure an effective reconstruction of the brain surface from a new brain dataset. Compared with conventional typical methods, such as level set based method and active contour method, our method significantly increases the recall and precision rates above 97% and is approximately hundreds-fold faster. We demonstrated a fast reconstruction at micron level of the whole brain surface from a large dataset of hundreds of GB in size within 6 hours.

Resampling

10.1038/srep12782

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Citations (3)