Wenjian Qin

Wenjian Qin
Chinese Academy of Sciences | CAS

Doctor of Philosophy

About

58
Publications
13,265
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648
Citations

Publications

Publications (58)
Article
Background and purpose Geometric information such as distance information is essential for dose calculations in radiotherapy. However, state-of-the-art dose prediction methods use only binary masks without distance information. This study aims to develop a dose prediction deep learning method for nasopharyngeal carcinoma radiotherapy by taking adva...
Article
Automatic classification of hepatic segments is of great use for liver surgical resection planning. However, conventional computer-aided annotation methods have difficulty annotating cases with weakly visible hepatic vascular structures. To address this issue, we proposed a class center attention convolutional neural network with spatial adaption....
Article
Background: In the radiotherapy of nasopharyngeal carcinoma (NPC), magnetic resonance imaging (MRI) is widely used to delineate tumor area more accurately. While MRI offers the higher soft tissue contrast, patient positioning and couch correction based on bony image fusion of computed tomography (CT) is also necessary. There is thus an urgent need...
Article
Full-text available
Modern conformal beam delivery techniques require image-guidance to ensure the prescribed dose to be delivered as planned. Recent advances in artificial intelligence (AI) have greatly augmented our ability to accurately localize the treatment target while sparing the normal tissues. In this paper, we review the applications of AI-based algorithms i...
Preprint
Reducing the radiation exposure for patients in Total-body CT scans has attracted extensive attention in the medical imaging community. Given the fact that low radiation dose may result in increased noise and artifacts, which greatly affected the clinical diagnosis. To obtain high-quality Total-body Low-dose CT (LDCT) images, previous deep-learning...
Article
Visual inspection cancer regions of hepatocellular carcinoma (HCC) by experienced pathologists in whole slide images (WSIs) is a challenging, labour-intensive, and time-consuming task due to WSI’s large scale and high-resolution. We have therefore introduced a weakly-supervised framework based on a multi-scale attention convolutional neural network...
Article
Full-text available
Whole slide imaging enables scanning entire stained-glass slides with high resolution into digital images for the tissue morphology/molecular pathology assessment and analysis, which has increased in adoption for both clinical and research applications. As an alternative to conventional optical microscopy, lensfree holography imaging, which offers...
Article
Background and Objective Real time localization and shape extraction of guide wire in fluoroscopic images plays a significant role in the image guided navigation during cerebral and cardiovascular interventions. Given the complexity of the non-rigid and sparse characteristics of guide wire structures, and the low SNR(Signal Noise Ratio) of fluorosc...
Article
Software-based methods can improve CT spatial resolution without changing the hardware of the scanner or increasing the radiation dose to the object. In this work, we aim to develop a deep learning (DL) based CT super-resolution (SR) method that can reconstruct low-resolution (LR) sinograms into high-resolution (HR) CT images. We mathematically ana...
Article
Full-text available
Background Conventional dynamic contrast enhanced (DCE) magnetic resonance (MR) hardly achieves a good imaging performance of arteries and lymph nodes in the breast area. Therefore, a new imaging method is needed for the assessment of breast arteries and lymph nodes. Methods We performed prospective research. The research included 52 patients aged...
Article
Multi-material decomposition (MMD) decomposes CT images into basis material images, and is a promising technique in clinical diagnostic CT to identify material compositions within the human body. MMD could be implemented on measurements obtained from spectral CT protocol, although spectral CT data acquisition is not readily available in most clinic...
Preprint
Computed Tomography (CT) is an advanced imaging technology used in many important applications. Here we present a deep-learning (DL) based CT super-resolution (SR) method that can reconstruct low-resolution (LR) sinograms into high resolution (HR) CT images. The method synergistically combines a SR model in sinogram domain, a deblur model in image...
Preprint
Full-text available
Feature selection is important in data representation and intelligent diagnosis. Elastic net is one of the most widely used feature selectors. However, the features selected are dependant on the training data, and their weights dedicated for regularized regression are irrelevant to their importance if used for feature ranking, that degrades the mod...
Article
Background and Objective Automatic functional region annotation of liver should be very useful for preoperative planning of liver resection in the clinical domain. However, many traditional computer-aided annotation methods based on anatomical landmarks or the vascular tree often fail to extract accurate liver segments. Furthermore, these methods a...
Preprint
Full-text available
Nowadays, it is feasible to collect massive features for quantitative representation and precision medicine, and thus, automatic ranking to figure out the most informative and discriminative ones becomes increasingly important. To address this issue, 42 feature ranking (FR) methods are integrated to form a MATLAB toolbox (matFR). The methods apply...
Article
Full-text available
Background: Precise patient setup is critical in radiation therapy. Medical imaging plays an essential role in patient setup. As compared to computed tomography (CT) images, magnetic resonance image (MRI) has high contrast for soft tissues, which becomes a promising imaging modality during treatment. In this paper, we proposed a method to synthesi...
Article
Full-text available
The pathological diagnosis of nasopharyngeal carcinoma (NPC) by various different pathologists is often inefficient and inconsistent. We have therefore introduced a deep learning algorithm into this process and compared the performance of the model with that of three pathologists with different level of experience to demonstrate its clinical value....
Article
Purpose: Segmentation of magnetic resonance images (MRI) of the left ventricle (LV) plays a key role in quantifying the volumetric functions of the heart, such as the area, volume, and ejection fraction. Traditionally, LV segmentation is performed manually by experienced experts, which is both time-consuming and prone to subjective bias. This stud...
Preprint
More attention is being paid for feature importance ranking (FIR), in particular when thousands of features can be extracted for intelligent diagnosis and personalized medicine. A large number of FIR approaches have been proposed, while few are integrated for comparison and real-life applications. In this study, a matlab toolbox is presented and a...
Article
Background: Magnetic resonance cine imaging is the accepted standard for cardiac functional assessment. Left ventricular (LV) segmentation plays a key role in volumetric functional quantification of the heart. Conventional manual analysis is time-consuming and observer-dependent. Automated segmentation approaches are needed to improve the clinical...
Conference Paper
Full-text available
The emergency of whole slide imaging (WSI) in digital pathology is becoming a routine clinical diagnosis for manycancers.However,manual cancer regions review in wsIs for diagnosisis labor-intensive and error-prone task due tol arge scale,high-resolution and complexityoftumor heterogeneity. In this paper, we propose a fully automatic cancer region r...
Article
Automatic polyp recognition in endoscopic images is challenging because of the low contrast between polyps and the surrounding area, the fuzzy and irregular polyp borders, and varying imaging light conditions. In this article, we propose a novel densely connected convolutional network with "unbalanced discriminant (UD)" loss and "category sensitive...
Article
Full-text available
Background: Shading artifact may lead to CT number inaccuracy, image contrast loss and spatial nonuniformity (SNU), which is considered as one of the fundamental limitations for volumetric CT (VCT) application. To correct the shading artifact, a novel approach is proposed using deep learning and an adaptive filter (AF). Methods: Firstly, we apply t...
Chapter
The challenge of convolutional networks (CNNs) for medical imaging analysis is to train the network model with limited well labeled dataset. Since a variational autoencoder (VAE) is able to learn the probability distribution on data for describing an observation in terms of its latent attributes by unsupervised manner, it has emerged as one of the...
Article
Full-text available
Purpose: Prostate cancer classification has a significant impact on the prognosis and treatment planning of patients. Currently, this classification is based on the Gleason score analysis of biopsied tissues, which is neither accurate nor risk free. This study aims to learn discriminative features in prostate images and assist physicians in classi...
Article
Full-text available
The aim of this study was to develop a fast and accurate beam hardening correction method by modeling physical interactions between X-ray photons and materials for computed tomography (CT) imaging. Methods: The nonlinear attenuation process of the X-ray projection was modeled by reprojecting a template image with the estimated polychromatic spectru...
Chapter
Colorectal cancer is the leading cause of cancer-related deaths. Most colorectal cancers are believed to arise from benign adenomatous polyps. Automatic methods for polyp detection with Wireless Capsule Endoscopy (WCE) images are desirable, but the results of current approaches are limited due to the problems of object rotation and high intra-class...
Article
Full-text available
Noninvasive blood glucose monitoring (NBGM) provides a promising solution for patients with diabetes with the advantages of painless and continuous monitoring. To better characterize the response of glucose to radio-frequency (RF) signals at low frequencies, the conductivity and relative permittivity of aqueous solutions with different glucose conc...
Article
Full-text available
Respiratory control is essential for treatment effect of radiotherapy due to the high dose, especially for thoracic-abdomen tumor, such as lung and liver tumors. As a noninvasive and comfortable way of respiratory control, hypnosis has been proven effective as a psychological technology in clinical therapy. In this study, the neural control mechani...
Article
Full-text available
Caused by strong winds or nonuniform icing, conductor galloping is one of the major hazards that should be monitored in a timely fashion. In this paper, we proposed a new full-scale reconstruction method for transmission line curves based on the attitude sensors that uses only the tangential information and arc-length constraint. Meanwhile, a compa...
Article
Full-text available
This research aims to address the problem of discriminating benign cysts from malignant masses in breast ultrasound (BUS) images based on Convolutional Neural Networks (CNNs). The biopsy-proven benchmarking dataset was built from 1422 patient cases containing a total of 2058 breast ultrasound masses, comprising 1370 benign and 688 malignant lesions...
Article
Full-text available
Segmentation of liver in abdominal computed tomography (CT) is an important step for radiation therapy planning of hepatocellular carcinoma. Practically, a fully automatic segmentation of liver remains challenging because of low soft tissue contrast between liver and its surrounding organs, and its highly deformable shape. The purpose of this work...
Article
Full-text available
Due to the low contrast and ambiguous boundaries of the tumors in breast ultrasound (BUS) images, it is still a challenging task to automatically segment the breast tumors from the ultrasound. In this paper, we proposed a novel computational framework that can detect and segment breast lesions fully automatic in the whole ultrasound images. This fr...
Conference Paper
Liver lesion detection is an important task for diagnosis and surgical planning of focal liver disease. The large numbers of images in routine liver CT studies, in addition to their high diversity in appearance, have been hurdles for detecting all lesions by visual inspection. Automated methods for lesion identification are desirable, but the resul...
Conference Paper
Full-text available
Volume reconstruction plays an important role in improving image quality for freehand three-dimensional (3D) ultrasound imaging. The kernel regression provides an effective method for volume reconstruction in 3D ultrasound imaging, but it requires heavily computational time-cost. In this paper, a programmable graphic-processor-unit-(GPU) based fast...
Article
Volume reconstruction method plays an important role in improving reconstructed volumetric image quality for freehand three-dimensional (3D) ultrasound imaging. By utilizing the capability of programmable graphics processing unit (GPU), we can achieve a real-time incremental volume reconstruction at a speed of 25-50 frames per second (fps). After i...
Article
Full-text available
In this paper, an approach to biometric verification based on human body communication (HBC) is presented for wearable devices. For this purpose, the transmission gain S21 of volunteer’s forearm is measured by vector network analyzer (VNA). Specifically, in order to determine the chosen frequency for biometric verification, 1800 groups of data are...
Article
Nowadays air quality data can be easily accumulated by sensors around the world. Analysis on air quality data is very useful for society decision. Among five major air pollutants which are calculated for AQI (Air Quality Index), PM2.5 data is the most concerned by the people. PM2.5 data is also cross-impacted with the other factors in the air and w...
Article
Ultrasound is one of the most important medical imaging modalities for its real-time and portable imaging advantages. However, the contrast resolution and important details are degraded by the speckle in ultrasound images. Many speckle filtering methods have been developed, but they are suffered from several limitations, difficult to reach a balanc...
Conference Paper
In histopathology images, there often exists several Nuclei overlapped with each other which causes difficulty to automatic nuclei segmentation. As we all know, watershed algorithm has been widely employed in image segmentation. But the limitation of watershed segmentation is sensitive to noise and can lead to serious over-segmentation. In this pap...
Conference Paper
Image segmentation plays a crucial role in breast ultrasound (BUS) for breast cancer detection. However, due to the heavy speckle noise, low contrast and shadowing effects of BUS images, it's a challenging task to develop an accurate and robust segmentation algorithm. In this paper, we present a novel algorithm for breast ultrasound image segmentat...
Article
Full-text available
Introduction Freehand three-dimensional (3D) ultrasound has the advantages of flexibility for allowing clinicians to manipulate the ultrasound probe over the examined body surface with less constraint in comparison with other scanning protocols. Thus it is widely used in clinical diagnose and image-guided surgery. However, as the data scanning of f...
Conference Paper
Active contour methods (ACM) are model-based approaches for image segmentation and were developed in the late 1980s. ACM can be divided into two classes: parametric active contour model and geometric active contour model. Geometric method is intrinsic model. Because of its completeness in mathematics, geometric active contour model overcomes many d...
Article
Full-text available
Abdominal organs segmentation of magnetic resonance (MR) images is an important but challenging task in medical image processing. Especially for abdominal tissues or organs, such as liver and kidney, MR imaging is a very difficult task due to the fact that MR images are affected by intensity inhomogeneity, weak boundary, noise and the presence of s...
Article
Full-text available
Freehand three-dimensional ultrasound imaging is a highly attractive research area because it is capable of volumetric visualization and analysis of tissues and organs. The reconstruction algorithm plays a key role to the construction of three-dimensional ultrasound volume data with higher image quality and faster reconstruction speed. However, a s...
Article
Since the phenomena of intensity inhomogeneity in MR images are prominent and adversely affect quantitative image analysis .In this paper ,we propose a novel magnetic resonance (MR) image segmentation approach based on the kernel graph cuts technique .Because of automatic multiregion segmentation and global energy minimization ,the kernel graph cut...
Article
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Background Computer-assisted surgical navigation aims to provide surgeons with anatomical target localization and critical structure observation, where medical image processing methods such as segmentation, registration and visualization play a critical role. Percutaneous renal intervention plays an important role in several minimally-invasive surg...
Article
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In this paper, we propose a novel method for 1D infrared camera distortion based on polynomial model. We derive a 1D camera calibration algorithm and then estimate the distortion parameters by means of minimum calibration error. The used polynomial model is linear, such that the estimation can be finished with low computation burden without losing...
Article
In recent years, Wireless capsule endoscopy (WCE) has been widely utilized in diagnosis of gastrointestinal (GI) tract disease. This new technology is painless and can see small intestine that traditional endoscopies cannot reach. However, Analysis of massive images for each WCE detection is tedious and time consuming to physicians. In this paper w...
Conference Paper
Simulation of Ultrasound (US) images from other image modalities like MRI (Magnetic Resonance Imaging) is playing increasingly important role on education training, image registration applications and minimally invasive procedures. However, the computation speed and implementation is still a great challenge in clinical application. In this paper, w...
Article
Full-text available
Three-dimensional (3D) ultrasound (US) is increasingly being introduced in the clinic, both for diagnostics and image guidance. Obtaining 3D volumes with 2D US probes is a two-step process. First, a positioning sensor must be attached to the probe; second, a reconstruction of a 3D volume can be performed into a regular voxel grid. Various algorithm...

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Projects (4)
Project
Medical image segmentation, including polyp segmentation, melanoma segmentation, prostate segmentation, brain tumor segmentation, etc.
Project
This project is to provide some tools to deepen our understanding of machine learning.
Project
Our purpose is to develop algorithms and frameworks for improving image quality, including but not limited to image interpolation, registration and segmentation, that facilitates image analysis.