About

Xin Chen is an Assistant Professor in Computer Science at the University of Nottingham. His research interests are image processing, computer vision and machine learning, particularly applied to medical image analysis. He has expertise in 2D/3D image segmentation, image registration, statistical shape/ motion modelling, classification/ regression models and CT & MRI image reconstruction, which have been successfully applied to different medical applications (e.g. breast cancer, diabetes care, wrist pathology and radiotherapy). He is an active member in the computer vision and medical imaging community, regularly served as reviewers for international conferences and prestigious journals (e.g. MICCAI, ISBI, BMVC, IEEE-TMI, IEEE-TBME, Physics in Medicine and Biology, etc.).

Projects

The wrist is one of the most complex and vulnerable joints in the body, consisting of eight carpal bones. Wrist pain is currently diagnosed by expert assessment of abnormal carpal bone movements in 2D fluoroscopy sequences. The overall aim of the current project is computer interpretation of these 2D sequences to recover the 3D motion of the carpal bones, and further leads to quantitative measurements of wrist diseases.

- Chen, X., et al., Automatic Inference and Measurement of 3D Carpal Bone Kinematics From Single View Fluoroscopic Sequences. IEEE Transactions on Medical Imaging, 2013. 32(2): p. 317-328.

- Chen, X., et al., Automatic Generation of Statistical Pose and Shape Models for Articulated Joints. IEEE Transactions on Medical Imaging, 2014. 33(2): p. 372 - 383.

- Chen, X., et al., Inferring 3D kinematics of carpal bones from single view fluoroscopic sequences, MICCAI 2011, Springer. p. 680-687.

- Chen, X., et al., Integrated frameworkfor simultaneous segmentation and registration of carpal bones, ICIP 2011, p. 433-436.

Corneal confocal microscopy is a novel in-vivo imaging modality that has the potential to be a non-invasive and objective image biomarker for peripheral neuropathy. Automatic quantification of nerve morphology is a major step forward in the early diagnosis and assessment of progression, and, in particular, for use in clinical trials to establish therapeutic benefit in diabetic and other peripheral neuropathies.

- Chen, X., et al., An Automatic Tool for Quantification of Nerve Fibers in Corneal Confocal Microscopy Images. IEEE Transactions on Biomedical Engineering, 2017. 64(4): p. 786-794.

- Chen, X., et al., Small Nerve Fiber Quantification in the Diagnosis of Diabetic Sensorimotor Polyneuropathy: Comparing Corneal Confocal Microscopy With Intraepidermal Nerve Fiber Density. Diabetes Care, 2015. 38(6): p. 1138-1144.

Currently, breast screening is almost exclusively performed with mammography. However, for women with dense breasts the sensitivity of mammography for detecting breast cancer is low. The aim of the project is to develop methods to personalise breast cancer screening, based on risk and breast density markers. We have deleloped method to estimated volumetric breast density from single view mammography.

- Chen, X., et al., Improving Mammographic Density Estimation in the Breast Periphery, IWDM 2016, p. 469-477

- Chen, X., et al., A Novel Framework for Fat, Glandular Tissue, Pectoral Muscle and Nipple Segmentation in Full Field Digital Mammograms, IWDM 2014. p. 201-208

- Chen, X., et al., Breast Cancer Risk Analysis Based on a Novel Segmentation Framework for Digital Mammograms, MICCAI 2014. p. 536-543.

Dynamic magnetic resonance imaging (MRI) involves imaging a region of interest with high temporal resolution, and is useful in many applications in which knowledge of motion is of interest. We present a novel retrospective self-gating method based on manifold alignment (MA), which enables reconstruction of free breathing, high spatial, and temporal resolution abdominal magnetic resonance imaging sequences.

- Chen, X., et al., High-Resolution Self-Gated Dynamic Abdominal MRI Using Manifold Alignment. IEEE Transactions on Medical Imaging, 2017. 36(4): p. 960 - 971.

- Chen, X., et al., Efficient deformable motion correction for 3-D abdominal MRI using manifold regression, MICCAI 2017, p. 270-278.

- Chen, X., et al., Dynamic Volume Reconstruction from Multi-slice Abdominal MRI Using Manifold Alignment, MICCAI 2016, p. 493-501.

Teaching

Mobile Device Programming


UG/PG Projects

Tools

Image segmentation is a crucial step in many medical image analysis processes. Manual or semi-automatic image segmentation is often necessary to provide accurate annotations for supervised machine learning algorithms or to be directly used for clinical feature quantification. Our software aims to enable rapid interactive image segmentation for both 2D and 3D medical images based on full-connected conditional random field method. It supports up to 10 foreground labels and various image format (Matlab, Nifty, DICOM, etc.). The software was developed in Matlab, hence the Matlab runtime library will be automatically installed. Please make sure you have internet connections during the installation process. The software is freely available for research purposes, but please send me an email with your affiliation details for password.

Software download.

Video instruction of the software.

Corneal confocal microscopy is a novel in-vivo imaging modality that has the potential to be a non-invasive and objective image biomarker for peripheral neuropathy. Automatic quantification of nerve morphology is a major step forward in the early diagnosis and assessment of progression, and, in particular, for use in clinical trials to establish therapeutic benefit in diabetic and other peripheral neuropathies.
The software has been recently updated using deep learning method. Simply download the tool from the link below, unzip it to your local drive, click "CCMAnalysis.exe" to run (no installation needed). It currently only support Windows machines.
If you find it useful, please cite our paper: N. Zhang et al., A spatially constrained deep convolutional neural network for nerve fiber segmentation in corneal confocal microscopic images using inaccurate annotations. IEEE-ISBI, 2020

Software download

Selected publications

Journals

- Chen, X., et al., Corneal nerve fractal dimension: a novel corneal nerve metric for the diagnosis of diabetic sensorimotor polyneuropathy. Investigative ophthalmology & visual science, 2018. 59(2): p. 1113-1118.

- Chen, X., et al., High-Resolution Self-Gated Dynamic Abdominal MRI Using Manifold Alignment. IEEE Transactions on Medical Imaging, 2017. 36(4): p. 960 - 971.

- Chen, X., et al., An Automatic Tool for Quantification of Nerve Fibers in Corneal Confocal Microscopy Images. IEEE Transactions on Biomedical Engineering, 2017. 64(4): p. 786-794.

- Chen, X., et al., Small Nerve Fiber Quantification in the Diagnosis of Diabetic Sensorimotor Polyneuropathy: Comparing Corneal Confocal Microscopy With Intraepidermal Nerve Fiber Density. Diabetes Care, 2015. 38(6): p. 1138-1144.

- Chen, X., et al., Automatic Generation of Statistical Pose and Shape Models for Articulated Joints. IEEE Transactions on Medical Imaging, 2014. 33(2): p. 372 - 383.

- Chen, X., et al., Automatic Inference and Measurement of 3D Carpal Bone Kinematics From Single View Fluoroscopic Sequences. IEEE Transactions on Medical Imaging, 2013. 32(2): p. 317-328.

- Chen, X., et al., A computationally efficient method for automatic registration of orthogonal x-ray images with volumetric CT data. Physics in Medicine & Biology, 2008. 53(4).

Conferences

- Chen, X., et al., Efficient deformable motion correction for 3-D abdominal MRI using manifold regression, MICCAI 2017, p. 270-278.

- Chen, X., et al., Dynamic Volume Reconstruction from Multi-slice Abdominal MRI Using Manifold Alignment, MICCAI 2016, p. 493-501.

- Chen, X., et al., Improving Mammographic Density Estimation in the Breast Periphery, IWDM 2016, p. 469-477

- Chen, X., et al., A Novel Framework for Fat, Glandular Tissue, Pectoral Muscle and Nipple Segmentation in Full Field Digital Mammograms, IWDM 2014. p. 201-208

- Chen, X., et al., Breast Cancer Risk Analysis Based on a Novel Segmentation Framework for Digital Mammograms, MICCAI 2014. p. 536-543.

- Chen, X., et al., Inferring 3D kinematics of carpal bones from single view fluoroscopic sequences, MICCAI 2011, Springer. p. 680-687.

- Chen, X., et al., Integrated frameworkfor simultaneous segmentation and registration of carpal bones, ICIP 2011, p. 433-436.

Team Members

Mina Jafari

PhD student

Golnar Mahani

PhD student

RuiZhe Li

PhD student