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学者姓名：郑绍华
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Atrial fibrillation (AF) is a prevalent heart rate arrhythmia and its incidence is increasing with the aging population. The late gadoliniumenhanced magnetic resonance imaging (LGE-MRI) provides pathologic changes in the left atrium, allowing for a detailed assessment of the left atrial anatomy, which is critical for diagnosis and treatment decisions in AF. The segmentation performance of current left atrial segmentation methods is significantly degraded when applied to multicenter data. In this work, we propose ResCAUNet, a deep learning method based on residual neural networks. Specifically, a pre-trained model driven residual segmentation network is first designed to alleviate the problem of gradient disappearance and help to extract more complex image features. Secondly, an adaptive scale weight loss function was introduced to solve the problem of discontinuous segmentation boundary, so as to ensure higher accuracy of object segmentation. Furthermore, the coordinate attention(CA) mechanism is introduced for adaptive weight allocation, focusing on the key parts of the image to improve the accuracy of left atrial reconstruction. Our method is evaluated on the LAScarQS2024 validation set and achieves an average Dice of 0.853. The evaluation results show that the proposed method is effective in left atrium segmentation of LGE-MRI. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Keyword ：
Deep neural networks Deep neural networks Diagnosis Diagnosis Diseases Diseases Image enhancement Image enhancement Image reconstruction Image reconstruction Image segmentation Image segmentation
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| GB/T 7714 | Li, Xinru , Gao, Ruikun , Zheng, Yuxin et al. A Left Atrial Automatic Segmentation Based on ResCAUNet [C] . 2025 : 139-148 . |
| MLA | Li, Xinru et al. "A Left Atrial Automatic Segmentation Based on ResCAUNet" . (2025) : 139-148 . |
| APA | Li, Xinru , Gao, Ruikun , Zheng, Yuxin , Zheng, Shaohua , Chen, Weisheng . A Left Atrial Automatic Segmentation Based on ResCAUNet . (2025) : 139-148 . |
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Cardiac scarring and edema are critical pathological features of heart diseases. Accurate segmentation of these features in Cardiac Magnetic Resonance (CMR) imaging is crucial for understanding the pathological changes associated with cardiac diseases. In the field of myocardial scar and edema segmentation, it is of significant importance to study the C0, T2, and LGE modalities. These modalities offer different perspectives on myocardial tissue characteristics, aiding in the more accurate diagnosis and assessment of cardiac diseases. However, the high-intensity features of scars and edema cannot be directly obtained from individual CMR imaging sequences, making simultaneous accurate segmentation challenging. To address this, we propose a multi-modal, multi-channel fusion interactive progressive segmentation strategy that leverages the distinctive properties of each modality and the surrounding tissue characteristics for the segmentation of myocardial scars and edema. We have designed a multi-channel fusion interactive progressive segmentation model, suitable for scar and myocardial segmentation, which incorporates an attention mechanism that enhances channel information interaction within a U-Net structure to extract features across different modalities. On the MyoPS++ 2024 public dataset, our method achieved an average Dice score of 0.5486 for scar segmentation and 0.6081 for the segmentation of both scars and edema. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Keyword ：
Cardiology Cardiology Diagnosis Diagnosis Diseases Diseases Image segmentation Image segmentation Magnetic resonance imaging Magnetic resonance imaging Pathology Pathology
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| GB/T 7714 | Wang, Jingyan , Gong, Xiaojuan , Jin, Tangruoyi et al. Progressive Multi-channel Fusion Network for Myocardial Pathology Segmentation on Multi-modality CMR Images [C] . 2025 : 192-201 . |
| MLA | Wang, Jingyan et al. "Progressive Multi-channel Fusion Network for Myocardial Pathology Segmentation on Multi-modality CMR Images" . (2025) : 192-201 . |
| APA | Wang, Jingyan , Gong, Xiaojuan , Jin, Tangruoyi , Gao, Ruikun , Zheng, Shaohua , Chen, Weisheng . Progressive Multi-channel Fusion Network for Myocardial Pathology Segmentation on Multi-modality CMR Images . (2025) : 192-201 . |
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针对现有均匀量化的连续消除列表(Successive Cancellation List,SCL)译码算法中存储资源消耗大、布线延迟高的问题,提出了一种采用 5 bit非均匀量化方案的SCL译码算法.该算法保留均匀量化中的对数似然比(Log-Like-lihood Ratio,LLR)迭代计算方法,采用5 bit非均匀量化LLR,在LLR计算模块中设计查找表(Look-Up-Table,LUT)转为6 bit均匀量化LLR用于计算.仿真结果表明,提出的 5 bit非均匀量化SCL译码相比于 6 bit均匀量化 SCL译码器,在码率R=1/2、列表宽度L=2 和L=4 时,误帧率(Frame Erasure Rate,FER)性能损失在0.1dB以内.在硬件资源消耗方面,与 6 bit均匀量化译码器相比,5 bit非均匀量化方案译码器在 L=2 时触发器(Flip-Flop,FF)和块随机存取存储器(Block Random Access Memory,BRAM)存储资源消耗分别减少了 10.9%和 22%,吞吐量增加了 24%;L=4 时 FF和BRAM分别减少了 10%和 18.1%,吞吐量增加了 17.5%.
Keyword ：
极化码 极化码 现场可编程逻辑门阵列 现场可编程逻辑门阵列 连续消除列表译码 连续消除列表译码 非均匀量化 非均匀量化
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| GB/T 7714 | 魏少圣 , 熊启金 , 郑绍华 et al. 基于非均匀量化的极化码SCL译码器FPGA实现 [J]. | 无线电通信技术 , 2024 , 50 (6) : 1200-1208 . |
| MLA | 魏少圣 et al. "基于非均匀量化的极化码SCL译码器FPGA实现" . | 无线电通信技术 50 . 6 (2024) : 1200-1208 . |
| APA | 魏少圣 , 熊启金 , 郑绍华 , 陈平平 . 基于非均匀量化的极化码SCL译码器FPGA实现 . | 无线电通信技术 , 2024 , 50 (6) , 1200-1208 . |
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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
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| 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 . |
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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
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| 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 . |
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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
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| 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 . |
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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
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| 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 . |
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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
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| 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) . |
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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
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| 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 . |
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Kidney cancer is one of the top ten cancers in the world, and its incidence is still increasing. Early detection and accurate treatment are the most effective control methods. The precise and automatic segmentation of kidney tumors in computed tomography (CT) is an important prerequisite for medical methods such as pathological localization and radiotherapy planning, However, due to the large differences in the shape, size, and location of kidney tumors, the accurate and automatic segmentation of kidney tumors still encounter great challenges. Recently, U-Net and its variants have been adopted to solve medical image segmentation problems. Although these methods achieved favorable performance, the long-range dependencies of feature maps learned by convolutional neural network (CNN) are overlooked, which leaves room for further improvement. In this paper, we propose an squeeze-and-excitation encoder-decoder network, named SeResUNet, for kidney and kidney tumor segmentation. SeResUNet is an U-Net-like architecture. The encoder of SeResUNet contains a SeResNet to learns high-level semantic features and model the long-range dependencies among different channels of the learned feature maps. The decoder is the same as the vanilla U-Net. The encoder and decoder are connected by the skip connections for feature concatenation. We used the kidney and kidney tumor segmentation 2021 dataset to evaluate the proposed method. The dice, surface dice and tumor dice score of SeResUNet are 67.2%, 54.4%, 54.5%, respectively.
Keyword ：
Kidney tumor segmentation Kidney tumor segmentation Squeeze-and-excitation network Squeeze-and-excitation network U-Net U-Net
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| GB/T 7714 | Wen, Jianhui , Li, Zhaopei , Shen, Zhiqiang et al. Squeeze-and-Excitation Encoder-Decoder Network for Kidney and Kidney Tumor Segmentation in CT Images [J]. | KIDNEY AND KIDNEY TUMOR SEGMENTATION, KITS 2021 , 2022 , 13168 : 71-79 . |
| MLA | Wen, Jianhui et al. "Squeeze-and-Excitation Encoder-Decoder Network for Kidney and Kidney Tumor Segmentation in CT Images" . | KIDNEY AND KIDNEY TUMOR SEGMENTATION, KITS 2021 13168 (2022) : 71-79 . |
| APA | Wen, Jianhui , Li, Zhaopei , Shen, Zhiqiang , Zheng, Yaoyong , Zheng, Shaohua . Squeeze-and-Excitation Encoder-Decoder Network for Kidney and Kidney Tumor Segmentation in CT Images . | KIDNEY AND KIDNEY TUMOR SEGMENTATION, KITS 2021 , 2022 , 13168 , 71-79 . |
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