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学者姓名：潘林
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Unsupervised domain adaptation(UDA) aims to mitigate the performance drop of models tested on the target domain, due to the domain shift from the target to sources. Most UDA segmentation methods focus on the scenario of solely single source domain. However, in practical situations data with gold standard could be available from multiple sources (domains), and the multi-source training data could provide more information for knowledge transfer. How to utilize them to achieve better domain adaptation yet remains to be further explored. This work investigates multi-source UDA and proposes a new framework for medical image segmentation. Firstly, we employ a multi-level adversarial learning scheme to adapt features at different levels between each of the source domains and the target, to improve the segmentation performance. Then, we propose a multi-model consistency loss to transfer the learned multi-source knowledge to the target domain simultaneously. Finally, we validated the proposed framework on two applications, i.e., multi-modality cardiac segmentation and cross-modality liver segmentation. The results showed our method delivered promising performance and compared favorably to state-of-the-art approaches. © 2023 IEEE.
Keyword ：
Job analysis Job analysis Knowledge management Knowledge management Medical imaging Medical imaging Semantics Semantics Semantic Segmentation Semantic Segmentation
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| GB/T 7714 | Pei, Chenhao , Wu, Fuping , Yang, Mingjing et al. Multi-Source Domain Adaptation for Medical Image Segmentation [J]. | IEEE Transactions on Medical Imaging , 2024 , 43 (4) : 1640-1651 . |
| MLA | Pei, Chenhao et al. "Multi-Source Domain Adaptation for Medical Image Segmentation" . | IEEE Transactions on Medical Imaging 43 . 4 (2024) : 1640-1651 . |
| APA | Pei, Chenhao , Wu, Fuping , Yang, Mingjing , Pan, Lin , Ding, Wangbin , Dong, Jinwei et al. Multi-Source Domain Adaptation for Medical Image Segmentation . | IEEE Transactions on Medical Imaging , 2024 , 43 (4) , 1640-1651 . |
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Given the diversity of medical images, traditional image segmentation models face the issue of domain shift. Unsupervised domain adaptation (UDA) methods have emerged as a pivotal strategy for cross modality analysis. These methods typically utilize generative adversarial networks (GANs) for both image-level and feature-level domain adaptation through the transformation and reconstruction of images, assuming the features between domains are well-aligned. However, this assumption falters with significant gaps between different medical image modalities, such as MRI and CT. These gaps hinder the effective training of segmentation networks with cross-modality images and can lead to misleading training guidance and instability. To address these challenges, this paper introduces a novel approach comprising a cross-modality feature alignment sub-network and a cross pseudo supervised dual-stream segmentation sub-network. These components work together to bridge domain discrepancies more effectively and ensure a stable training environment. The feature alignment sub-network is designed for the bidirectional alignment of features between the source and target domains, incorporating a self-attention module to aid in learning structurally consistent and relevant information. The segmentation sub-network leverages an enhanced cross-pseudo-supervised loss to harmonize the output of the two segmentation networks, assessing pseudo-distances between domains to improve the pseudo-label quality and thus enhancing the overall learning efficiency of the framework. This method's success is demonstrated by notable advancements in segmentation precision across target domains for abdomen and brain tasks.
Keyword ：
cross modality segmentation cross modality segmentation cross pseudo supervision cross pseudo supervision feature alignment feature alignment unsupervised domain adaptation unsupervised domain adaptation
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| GB/T 7714 | Yang, Mingjing , Wu, Zhicheng , Zheng, Hanyu et al. Cross-Modality Medical Image Segmentation via Enhanced Feature Alignment and Cross Pseudo Supervision Learning [J]. | DIAGNOSTICS , 2024 , 14 (16) . |
| MLA | Yang, Mingjing et al. "Cross-Modality Medical Image Segmentation via Enhanced Feature Alignment and Cross Pseudo Supervision Learning" . | DIAGNOSTICS 14 . 16 (2024) . |
| APA | Yang, Mingjing , Wu, Zhicheng , Zheng, Hanyu , Huang, Liqin , Ding, Wangbin , Pan, Lin et al. Cross-Modality Medical Image Segmentation via Enhanced Feature Alignment and Cross Pseudo Supervision Learning . | DIAGNOSTICS , 2024 , 14 (16) . |
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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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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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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
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| 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]. |
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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
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| 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]. |
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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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结肠镜检查广泛应用于结直肠癌的早期筛查和诊疗，但仅靠人工判读结肠息肉漏检率较高，有研究统计可达25%。基于深度学习的计算机辅助技术有助于提高息肉检测率，但目前深度学习的主流分割网络U-Net存在着两个局限：一是编解码的输出特征图之间存在着语义鸿沟；二是U-Net的双层卷积单元无法学习多尺度信息；割裂地看待容易使模型陷入局部最优。提出一种基于同构化改进的U-Net网络，不仅能缓解编解码特征间的语义鸿沟，且具备提取多尺度特征的能力。首先，在U-Net编解码器和跳层路径中，引入同构单元IU构成同构网络I-Net,以减少编解码器之间的语义鸿沟；接着，结合密集连接和残差连接的优点，设计密集残差单元DRU...
Keyword ：
同构网络 同构网络 息肉分割 息肉分割 深度学习 深度学习
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| GB/T 7714 | 沈志强 , 林超男 , 潘林 et al. 基于同构化改进的U-Net结直肠息肉分割方法 [J]. | 中国生物医学工程学报 , 2022 , 41 (01) : 48-56 . |
| MLA | 沈志强 et al. "基于同构化改进的U-Net结直肠息肉分割方法" . | 中国生物医学工程学报 41 . 01 (2022) : 48-56 . |
| APA | 沈志强 , 林超男 , 潘林 , 聂炜宇 , 裴玥 , 黄立勤 et al. 基于同构化改进的U-Net结直肠息肉分割方法 . | 中国生物医学工程学报 , 2022 , 41 (01) , 48-56 . |
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随着低剂量CT在肺癌筛查中的普遍应用，胸腔镜肺段切除术现已逐步应用于早期肺癌的治疗，因而精准解剖划分肺段是进行肺段切除术的前提与关键。目前，肺段划分的方法主要是通过三维CT支气管成像技术、分级标注实现的，但该方法受限于三维支气管重建结果。鉴于肺动脉远离肺门，段级肺动脉紧密伴行于支气管，且肺动脉相对于支气管有着更丰富的解剖结构特征。基于此，提出了一种基于肺动脉分级引导的肺段划分方法，通过肺动脉自动分级标注，对肺叶上每个体素计算其最邻近节段子树并将其归属到相应的肺段。经实验验证，该方法能够有效进行肺动脉分级标注，引导划分的肺段有良好的均衡性，能弥补气管分割不足造成的肺段划分较差问题。该方法在临床实...
Keyword ：
分级标注 分级标注 拓扑 拓扑 肺动脉 肺动脉 肺段 肺段 计算机断层扫描 计算机断层扫描
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| GB/T 7714 | 郑耀湧 , 潘林 . 基于肺动脉分级引导的肺段划分方法 [J]. | 信息技术与信息化 , 2022 , 4 (06) : 83-86 . |
| MLA | 郑耀湧 et al. "基于肺动脉分级引导的肺段划分方法" . | 信息技术与信息化 4 . 06 (2022) : 83-86 . |
| APA | 郑耀湧 , 潘林 . 基于肺动脉分级引导的肺段划分方法 . | 信息技术与信息化 , 2022 , 4 (06) , 83-86 . |
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