Query：
学者姓名：潘林
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
Objectives Patients with T4 obstructive colorectal cancer (OCC) have a high mortality rate. Therefore, an accurate distinction between T4 and T1-T3 (NT4) in OCC is an important part of preoperative evaluation, especially in the emergency setting. This paper introduces three models of radiomics, deep learning, and deep learning-based radiomics to identify T4 OCC.Methods We established a dataset of computed tomography (CT) images of 164 patients with pathologically confirmed OCC, from which 2537 slides were extracted. First, since T4 tumors penetrate the bowel wall and involve adjacent organs, we explored whether the peritumoral region contributes to the assessment of T4 OCC. Furthermore, we visualized the radiomics and deep learning features using the t-distributed stochastic neighbor embedding technique (t-SNE). Finally, we built a merged model by fusing radiomic features with deep learning features. In this experiment, the performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC).Results In the test cohort, the AUC values predicted by the radiomics model in the dilated region of interest (dROI) was 0.770. And the AUC value of the deep learning model with the patches extended 20-pixel reached 0.936. Combining the characteristics of radiomics and deep learning, our method achieved an AUC value of 0.947 in the T4 and non-T4 (NT4) classification, and increased the AUC value to 0.950 after the addition of clinical features.Conclusion The prediction results of our merged model of deep learning radiomics outperformed the deep learning model and significantly outperformed the radiomics model. The experimental results demonstrate that combining the peritumoral region improves the prediction performance of the radiomics model and the deep learning model.
Keyword ：
Deep learning Deep learning Obstructive colorectal cancer Obstructive colorectal cancer Peritumoral region Peritumoral region Radiomics Radiomics ResNet ResNet
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Pan, Lin , He, Tian , Huang, Zihan et al. Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image [J]. | ABDOMINAL RADIOLOGY , 2023 , 48 (4) : 1246-1259 . |
| MLA | Pan, Lin et al. "Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image" . | ABDOMINAL RADIOLOGY 48 . 4 (2023) : 1246-1259 . |
| APA | Pan, Lin , He, Tian , Huang, Zihan , Chen, Shuai , Zhang, Junrong , Zheng, Shaohua et al. Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image . | ABDOMINAL RADIOLOGY , 2023 , 48 (4) , 1246-1259 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Background: With the wide application of CT scanning, the separation of pulmonary arteries and veins (A/V) based on CT images plays an important role for assisting surgeons in preoperative planning of lung cancer surgery. However, distinguishing between arteries and veins in chest CT images remains challenging due to the complex structure and the presence of their similarities. Methods: We proposed a novel method for automatically separating pulmonary arteries and veins based on vessel topology information and a twin-pipe deep learning network. First, vessel tree topology is constructed by combining scale-space particles and multi-stencils fast marching (MSFM) methods to ensure the continuity and authenticity of the topology. Second, a twin-pipe network is designed to learn the multiscale differences between arteries and veins and the characteristics of the small arteries that closely accompany bronchi. Finally, we designed a topology optimizer that considers interbranch and intrabranch topological relationships to optimize the results of arteries and veins classification. Results: The proposed approach is validated on the public dataset CARVE14 and our private dataset. Compared with ground truth, the proposed method achieves an average accuracy of 90.1% on the CARVE14 dataset, and 96.2% on our local dataset. Conclusions: The method can effectively separate pulmonary arteries and veins and has good generalization for chest CT images from different devices, as well as enhanced and noncontrast CT image sequences from the same device.
Keyword ：
Chest CT images Chest CT images Preoperative planning Preoperative planning Pulmonary artery-vein segmentation Pulmonary artery-vein segmentation Topology reconstruction Topology reconstruction Twin-pipe network Twin-pipe network
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Pan, Lin , Yan, Xiaochao , Zheng, Yaoyong et al. Automatic pulmonary artery-vein separation in CT images using a twin-pipe network and topology reconstruction [J]. | PEERJ COMPUTER SCIENCE , 2023 , 9 . |
| MLA | Pan, Lin et al. "Automatic pulmonary artery-vein separation in CT images using a twin-pipe network and topology reconstruction" . | PEERJ COMPUTER SCIENCE 9 (2023) . |
| APA | Pan, Lin , Yan, Xiaochao , Zheng, Yaoyong , Huang, Liqin , Zhang, Zhen , Fu, Rongda et al. Automatic pulmonary artery-vein separation in CT images using a twin-pipe network and topology reconstruction . | PEERJ COMPUTER SCIENCE , 2023 , 9 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Background: Automatic pulmonary artery-vein separation has considerable importance in the diagnosis and treatment of lung diseases. However, insufficient connectivity and spatial inconsistency have always been the problems of artery-vein separation. Methods: A novel automatic method for artery-vein separation in CT images is presented in this work. Specifically, a multi-scale information aggregated network (MSIA-Net) including multi-scale fusion blocks and deep supervision, is proposed to learn the features of artery-vein and aggregate additional semantic information, respectively. The proposed method integrates nine MSIA-Net models for artery-vein separation, vessel segmentation, and centerline separation tasks along with axial, coronal, and sagittal multi-view slices. First, the preliminary artery-vein separation results are obtained by the proposed multi-view fusion strategy (MVFS). Then, centerline correction algorithm (CCA) is used to correct the preliminary results of artery- vein separation by the centerline separation results. Finally, the vessel segmentation results are utilized to reconstruct the artery-vein morphology. In addition, weighted cross-entropy and dice loss are employed to solve the class imbalance problem. Results: We constructed 50 manually labeled contrast-enhanced computed CT scans for five-fold cross -validation, and experimental results demonstrated that our method achieves superior segmentation perfor-mance of 97.7%, 85.1%, and 84.9% on ACC, Pre, and DSC, respectively. Additionally, a series of ablation studies demonstrate the effectiveness of the proposed components. Conclusion: The proposed method can effectively solve the problem of insufficient vascular connectivity and correct the spatial inconsistency of artery-vein.
Keyword ：
Centerline correction Centerline correction CT images CT images Multi-scale information aggregated Multi-scale information aggregated Pulmonary artery-vein separation Pulmonary artery-vein separation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Pan, Lin , Li, Zhaopei , Shen, Zhiqiang et al. Learning multi-view and centerline topology connectivity information for pulmonary artery-vein separation [J]. | COMPUTERS IN BIOLOGY AND MEDICINE , 2023 , 155 . |
| MLA | Pan, Lin et al. "Learning multi-view and centerline topology connectivity information for pulmonary artery-vein separation" . | COMPUTERS IN BIOLOGY AND MEDICINE 155 (2023) . |
| APA | Pan, Lin , Li, Zhaopei , Shen, Zhiqiang , Liu, Zheng , Huang, Liqin , Yang, Mingjing et al. Learning multi-view and centerline topology connectivity information for pulmonary artery-vein separation . | COMPUTERS IN BIOLOGY AND MEDICINE , 2023 , 155 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Diabetic retinopathy (DR) is a common diabetic complication that can lead to blindness in severe cases. Ultra-wide (swept source) optical coherence tomography angiography(UW-OCTA) imaging can help ophthalmologists in the diagnosis of DR. Automatic and accurate segmentation of the lesion area is essential in the diagnosis of DR. However, there still remain several challenges for accurately segmenting lesion areas from UW-OCTA: the various lesion locations, diverse morphology and blurred contrast. To solve these problems, in this paper, we propose a novel framework to segment neovascularization(NV), nonperfusion areas(NA) and intraretinal microvascular abnormalities(IMA), which consists of two parts: 1) We respectively input the images of three lesions into three different channels to achieve three different lesions segmentation at the same time; 2) We improve the traditional 2D U-Net by adding the residual module and dilated convolution. We evaluate the proposed method on the Diabetic Retinopathy Analysis Challenge (DRAC) in MICCAI2022. The mean Dice and mean IoU obtained by the method in the test cases are 0.4757 and 0.3538, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keyword ：
Diabetic Retinopathy Diabetic Retinopathy Segmentation Network Segmentation Network UW-OCTA UW-OCTA
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Chen, X. , Chen, Y. , Lin, C. et al. An Improved U-Net for Diabetic Retinopathy Segmentation [未知]. |
| MLA | Chen, X. et al. "An Improved U-Net for Diabetic Retinopathy Segmentation" [未知]. |
| APA | Chen, X. , Chen, Y. , Lin, C. , Pan, L. . An Improved U-Net for Diabetic Retinopathy Segmentation [未知]. |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Diabetic retinopathy (DR) is a chronic complication of diabetes that damages the retina and is one of the leading causes of blindness. In the process of diabetic retinopathy analysis, it is necessary to first assess the quality of images and select the images with better imaging quality. Then DR analysis, such as DR grading, is performed. Therefore, it is crucial to implement a flexible and robust method to achieve automatic image quality assessment and DR grading. In deep learning, due to the high complexity, weak individual differences, and noise interference of ultra-wide optical coherence tomography angiography (UW-OCTA) images, individual classification networks have not been able to achieve satisfactory accuracy on such tasks and do not generalize well. Therefore, in this work, we use multiple models ensemble methods, by ensemble different baseline networks of RegNet and EfficientNetV2, which can simply and significantly improve the prediction accuracy and robustness. A transfer learning based solution is proposed for the problem of insufficient diabetic image data for retinopathy. After doing feature enhancement on the images, the UW-OCTA image task will be fine-tuned by combining the network pre-trained with ImageNet data. our method achieves a quadratic weighted kappa of 0.778 and AUC of 0.887 in image quality assessment (IQA) and 0.807 kappa and AUC of 0.875 in diabetic retinopathy grading. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keyword ：
Diabetic Retinopathy Grading Diabetic Retinopathy Grading Image Quality Assessment Image Quality Assessment Model Ensemble Model Ensemble Transfer Learning Transfer Learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yan, X. , Li, Z. , Wen, J. et al. A Transfer Learning Based Model Ensemble Method for Image Quality Assessment and Diabetic Retinopathy Grading [未知]. |
| MLA | Yan, X. et al. "A Transfer Learning Based Model Ensemble Method for Image Quality Assessment and Diabetic Retinopathy Grading" [未知]. |
| APA | Yan, X. , Li, Z. , Wen, J. , Pan, L. . A Transfer Learning Based Model Ensemble Method for Image Quality Assessment and Diabetic Retinopathy Grading [未知]. |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Purpose: The identification of early-stage Parkinson's disease (PD) is important for the effective management of patients, affecting their treatment and prognosis. Recently, structural brain networks (SBNs) have been used to diagnose PD. However, how to mine abnormal patterns from high-dimensional SBNs has been a challenge due to the complex topology of the brain. Meanwhile, the existing prediction mechanisms of deep learning models are often complicated, and it is difficult to extract effective interpretations. In addition, most works only focus on the classification of imaging and ignore clinical scores in practical applications, which limits the ability of the model. Inspired by the regional modularity of SBNs, we adopted graph learning from the perspective of node clustering to construct an interpretable framework for PD classification.Methods: In this study, a multi-task graph structure learning framework based on node clustering (MNC-Net) is proposed for the early diagnosis of PD. Specifically, we modeled complex SBNs into modular graphs that facilitated the representation learning of abnormal patterns. Traditional graph neural networks are optimized through graph structure learning based on node clustering, which identifies potentially abnormal brain regions and reduces the impact of irrelevant noise. Furthermore, we employed a regression task to link clinical scores to disease classification, and incorporated latent domain information into model training through multi-task learning.Results: We validated the proposed approach on the Parkinsons Progression Markers Initiative dataset. Exper-imental results showed that our MNC-Net effectively separated the early-stage PD from healthy controls(HC) with an accuracy of 95.5%. The t-SNE figures have showed that our graph structure learning method can capture more efficient and discriminatory features. Furthermore, node clustering parameters were used as important weights to extract salient task-related brain regions(ROIs). These ROIs are involved in the development of mood disorders, tremors, imbalances and other symptoms, highlighting the importance of memory, language and mild motor function in early PD. In addition, statistical results from clinical scores confirmed that our model could capture abnormal connectivity that was significantly different between PD and HC. These results are consistent with previous studies, demonstrating the interpretability of our methods.
Keyword ：
Clinical scores Clinical scores Early Parkinson?s disease Early Parkinson?s disease Graph neural networks Graph neural networks Structural brain network Structural brain network
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Huang, Liqin , Ye, Xiaofang , Yang, Mingjing et al. MNC-Net: Multi-task graph structure learning based on node clustering for early Parkinson's disease diagnosis [J]. | COMPUTERS IN BIOLOGY AND MEDICINE , 2023 , 152 . |
| MLA | Huang, Liqin et al. "MNC-Net: Multi-task graph structure learning based on node clustering for early Parkinson's disease diagnosis" . | COMPUTERS IN BIOLOGY AND MEDICINE 152 (2023) . |
| APA | Huang, Liqin , Ye, Xiaofang , Yang, Mingjing , Pan, Lin , Zheng, Shao hua . MNC-Net: Multi-task graph structure learning based on node clustering for early Parkinson's disease diagnosis . | COMPUTERS IN BIOLOGY AND MEDICINE , 2023 , 152 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Lane detection is challenging under varied light conditions (e.g., night, shadow, and dazzling light) because a lane becomes blurred and extracting features becomes more difficult. Some researchers have proposed methods based on multitask learning and contextual information to solve this problem; however, these methods result in additional computing. A data enhancement method based on retinex theory is proposed. This method improves the adaptability of a lane model under varied light conditions. In particular, we design an image enhancement network for calculating the reflectivity of images, modifying their exposure, and then generating images with consistent exposure. These images are fed to the lane detection model for training and detection. Our network consists of two parts: exposure-consistent image generation and lane detection. We validate our method on the CULane dataset, and results show that it can improve lane detection performance, particularly on light-related datasets. (C) 2022 SPIE and IS&T
Keyword ：
data enhancement data enhancement exposure consistency exposure consistency retinex theory retinex theory varied light lane detection varied light lane detection
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yang, Mingjing , Wei, Yingdong , Pan, Lin et al. Exposure consistency for lane detection under varied light conditions [J]. | JOURNAL OF ELECTRONIC IMAGING , 2022 , 31 (3) . |
| MLA | Yang, Mingjing et al. "Exposure consistency for lane detection under varied light conditions" . | JOURNAL OF ELECTRONIC IMAGING 31 . 3 (2022) . |
| APA | Yang, Mingjing , Wei, Yingdong , Pan, Lin , Huang, Liqin . Exposure consistency for lane detection under varied light conditions . | JOURNAL OF ELECTRONIC IMAGING , 2022 , 31 (3) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Automatic segmentation of multiple organs is a challenging topic. Most existing approaches are based on 2D network or 3D network, which leads to insufficient contextual exploration in organ segmentation. In recent years, many methods for automatic segmentation based on fully supervised deep learning have been proposed. However, it is very expensive and time-consuming for experienced medical practitioners to annotate a large number of pixels. In this paper, we propose a new two-dimensional multi slices semi-supervised method to perform the task of abdominal organ segmentation. The network adopts the information along the z-axis direction in CT images, preserves and exploits the useful temporal information in adjacent slices. Besides, we combine Cross-Entropy Loss and Dice Loss as loss functions to improve the performance of our method. We apply a teacher-student model with Exponential Moving Average (EMA) strategy to leverage the unlabeled data. The student model is trained with labeled data, and the teacher model is obtained by smoothing the student model weights via EMA. The pseudo-labels of unlabeled images predicted by the teacher model are used to train the student model as the final model. The mean DSC for all cases we obtained on the validation set was 0.5684, the mean NSD was 0.5971, and the total run time was 783.14 s. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keyword ：
Computer aided instruction Computer aided instruction Computerized tomography Computerized tomography Deep learning Deep learning Medical imaging Medical imaging Students Students Supervised learning Supervised learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Chen, Hao , Zhang, Wen , Yan, Xiaochao et al. Multi-organ Segmentation Based on 2.5D Semi-supervised Learning [C] . 2022 : 74-86 . |
| MLA | Chen, Hao et al. "Multi-organ Segmentation Based on 2.5D Semi-supervised Learning" . (2022) : 74-86 . |
| APA | Chen, Hao , Zhang, Wen , Yan, Xiaochao , Chen, Yanbin , Chen, Xin , Wu, Mengjun et al. Multi-organ Segmentation Based on 2.5D Semi-supervised Learning . (2022) : 74-86 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Automatic segmentation and centerline extraction of blood vessels from retinal fundus images is an essential step to measure the state of retinal blood vessels and achieve the goal of auxiliary diagnosis. Combining the information of blood vessel segments and centerline can help improve the continuity of results and performance. However, previous studies have usually treated these two tasks as separate research topics. Therefore, we propose a novel multitask learning network (MSC-Net) for retinal vessel segmentation and centerline extraction. The network uses a multibranch design to combine information between two tasks. Channel and atrous spatial fusion block (CAS-FB) is designed to fuse and correct the features of different branches and different scales. The clDice loss function is also used to constrain the topological continuity of blood vessel segments and centerline. Experimental results on different fundus blood vessel datasets (DRIVE, STARE, and CHASE) show that our method can obtain better segmentation and centerline extraction results at different scales and has better topological continuity than state-of-the-art methods.
Keyword ：
centerline extraction centerline extraction multitask learning multitask learning retinal fundus images retinal fundus images vessel segmentation vessel segmentation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Pan, Lin , Zhang, Zhen , Zheng, Shaohua et al. MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction [J]. | APPLIED SCIENCES-BASEL , 2022 , 12 (1) . |
| MLA | Pan, Lin et al. "MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction" . | APPLIED SCIENCES-BASEL 12 . 1 (2022) . |
| APA | Pan, Lin , Zhang, Zhen , Zheng, Shaohua , Huang, Liqin . MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction . | APPLIED SCIENCES-BASEL , 2022 , 12 (1) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Pulmonary nodule detection with low-dose computed tomography (LDCT) is indispensable in early lung cancer screening. Although existing methods have achieved excellent detection sensitivity, nodule detection still faces challenges such as nodule size variation and uneven distribution, as well as excessive nodule-like false positive candidates in the detection results. We propose a novel two-stage nodule detection (TSND) method. In the first stage, a multi-scale feature detection network (MSFD-Net) is designed to generate nodule candidates. This includes a proposed feature extraction network to learn the multi-scale feature representation of candidates. In the second stage, a candidate scoring network (CS-Net) is built to estimate the score of candidate patches to realize false positive reduction (FPR). Finally, we develop an end-to-end nodule computer-aided detection (CAD) system based on the proposed TSND for LDCT scans. Experimental results on the LUNA16 dataset show that our proposed TSND obtained an excellent average sensitivity of 90.59% at seven predefined false positives (FPs) points: 0.125, 0.25, 0.5, 1, 2, 4, and 8 FPs per scan on the FROC curve introduced in LUNA16. Moreover, comparative experiments indicate that our CS-Net can effectively suppress false positives and improve the detection performance of TSND.
Keyword ：
computer-aided detection system computer-aided detection system convolutional neural network convolutional neural network false positive reduction false positive reduction multi-scale object detection multi-scale object detection pulmonary nodule detection pulmonary nodule detection
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zheng, Shaohua , Kong, Shaohua , Huang, Zihan et al. A Lower False Positive Pulmonary Nodule Detection Approach for Early Lung Cancer Screening [J]. | DIAGNOSTICS , 2022 , 12 (11) . |
| MLA | Zheng, Shaohua et al. "A Lower False Positive Pulmonary Nodule Detection Approach for Early Lung Cancer Screening" . | DIAGNOSTICS 12 . 11 (2022) . |
| APA | Zheng, Shaohua , Kong, Shaohua , Huang, Zihan , Pan, Lin , Zeng, Taidui , Bin Zheng et al. A Lower False Positive Pulmonary Nodule Detection Approach for Early Lung Cancer Screening . | DIAGNOSTICS , 2022 , 12 (11) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
