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学者姓名：黄立勤
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Abstract ：
BACKGROUND The degree of obstruction plays an important role in decision-making for obstructive colorectal cancer (OCRC). The existing assessment still relies on the colorectal obstruction scoring system (CROSS) which is based on a comprehensive analysis of patients' complaints and eating conditions. The data collection relies on subjective descriptions and lacks objective parameters. Therefore, a scoring system for the evaluation of computed tomography-based obstructive degree (CTOD) is urgently required for OCRC. AIM To explore the relationship between CTOD and CROSS and to determine whether CTOD could affect the short-term and long-term prognosis. METHODS Of 173 patients were enrolled. CTOD was obtained using k-means, the ratio of proximal to distal obstruction, and the proportion of nonparenchymal areas at the site of obstruction. CTOD was integrated with the CROSS to analyze the effect of emergency intervention on complications. Short-term and long-term outcomes were compared between the groups. RESULTS CTOD severe obstruction (CTOD grade 3) was an independent risk factor [odds ratio (OR) = 3.390, 95% confidence interval (CI): 1.340-8.570, P = 0.010] via multivariate analysis of short-term outcomes, while CROSS grade was not. In the CTOD-CROSS grade system, for the non-severe obstructive (CTOD 1-2 to CROSS 1-4) group, the complication rate of emergency interventions was significantly higher than that of non-emergency interventions (71.4% vs 41.8%, P = 0.040). The postoperative pneumonia rate was higher in the emergency intervention group than in the non-severe obstructive group (35.7% vs 8.9%, P = 0.020). However, CTOD grade was not an independent risk factor of overall survival and progression-free survival. CONCLUSION CTOD was useful in preoperative decision-making to avoid unnecessary emergency interventions and complications.
Keyword ：
Colorectal obstruction scoring system Colorectal obstruction scoring system Computed tomography-based obstructive degree Computed tomography-based obstructive degree Emergency intervention Emergency intervention Obstructive colorectal cancer Obstructive colorectal cancer Scoring system Scoring system
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| GB/T 7714 | Shang-Guan, Xin-Chang , Zhang, Jun-Rong , Lin, Chao-Nan et al. New scoring system for the evaluation obstructive degrees based on computed tomography for obstructive colorectal cancer [J]. | WORLD JOURNAL OF GASTROINTESTINAL ONCOLOGY , 2025 , 17 (3) . |
| MLA | Shang-Guan, Xin-Chang et al. "New scoring system for the evaluation obstructive degrees based on computed tomography for obstructive colorectal cancer" . | WORLD JOURNAL OF GASTROINTESTINAL ONCOLOGY 17 . 3 (2025) . |
| APA | Shang-Guan, Xin-Chang , Zhang, Jun-Rong , Lin, Chao-Nan , Chen, Shuai , Wei, Yong , Chen, Wen-Xuan et al. New scoring system for the evaluation obstructive degrees based on computed tomography for obstructive colorectal cancer . | WORLD JOURNAL OF GASTROINTESTINAL ONCOLOGY , 2025 , 17 (3) . |
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Myocardium scar segmentation is essential for clinical diagnosis and prognosis for cardiac vascular diseases. Late gadolinium enhancement (LGE) imaging technology has been widely utilized to visualize left atrial and ventricular scars. However, automatic scar segmentation remains challenging due to the imbalance between scar and background and the variation in scar sizes. To address these challenges, we introduce an innovative network, i.e., LGENet, for scar segmentation. LGENet disentangles anatomy and pathology features from LGE images. Note that inherent spatial relationships exist between the myocardium and scarring regions. We proposed a boundary attention module to allow the scar segmentation conditioned on anatomical boundary features, which could mitigate the imbalance problem. Meanwhile, LGENet can predict scar regions across multiple scales with a multi-depth decision module, addressing the scar size variation issue. In our experiments, we thoroughly evaluated the performance of LGENet using LAScarQS 2022 and EMIDEC datasets. The results demonstrate that LGENet achieved promising performance for cardiac scar segmentation.
Keyword ：
Adaptive decision Adaptive decision Boundary attention Boundary attention Multi-depth network Multi-depth network Scar segmentation Scar segmentation
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| GB/T 7714 | Yang, Mingjing , Yang, Kangwen , Wu, Mengjun et al. LGENet: disentangle anatomy and pathology features for late gadolinium enhancement image segmentation [J]. | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING , 2025 , 63 (8) : 2311-2323 . |
| MLA | Yang, Mingjing et al. "LGENet: disentangle anatomy and pathology features for late gadolinium enhancement image segmentation" . | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 63 . 8 (2025) : 2311-2323 . |
| APA | Yang, Mingjing , Yang, Kangwen , Wu, Mengjun , Huang, Liqin , Ding, Wangbin , Pan, Lin et al. LGENet: disentangle anatomy and pathology features for late gadolinium enhancement image segmentation . | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING , 2025 , 63 (8) , 2311-2323 . |
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Transformers have been widely used in image dehazing tasks due to their powerful self-attention mechanism for capturing long-range dependencies. However, directly applying Transformers often leads to coarse details during image reconstruction, especially in complex real-world hazy scenarios. To address this problem, we propose a novel Hybrid Attention Encoder (HAE). Specifically, a channel-attention-based convolution block is integrated into the Swin-Transformer architecture. This design enhances the local features at each position through an overlapping block-wise spatial attention mechanism while leveraging the advantages of channel attention in global information processing to strengthen the network's representation capability. Moreover, to adapt to various complex hazy environments, a high-quality codebook prior encapsulating the color and texture knowledge of high-resolution clear scenes is introduced. We also propose a more flexible Binary Matching Mechanism (BMM) to better align the codebook prior with the network, further unlocking the potential of the model. Extensive experiments demonstrate that our method consistently outperforms the second-best methods by a margin of 8% to 19% across multiple metrics on the RTTS and URHI datasets. The source code has been released at https://github.com/HanyuZheng25/HADehzeNet.
Keyword ：
Channel attention Channel attention Discrete codebook learning Discrete codebook learning Single image dehazing Single image dehazing Swin-transformer Swin-transformer
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| GB/T 7714 | Huang, Liqin , Zheng, Hanyu , Pan, Lin et al. Codebook prior-guided hybrid attention dehazing network [J]. | IMAGE AND VISION COMPUTING , 2025 , 162 . |
| MLA | Huang, Liqin et al. "Codebook prior-guided hybrid attention dehazing network" . | IMAGE AND VISION COMPUTING 162 (2025) . |
| APA | Huang, Liqin , Zheng, Hanyu , Pan, Lin , Su, Zhipeng , Wu, Qiang . Codebook prior-guided hybrid attention dehazing network . | IMAGE AND VISION COMPUTING , 2025 , 162 . |
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Airway segmentation and reconstruction are critical for preoperative lesion localization and surgical planning in pulmonary interventions. However, this task remains challenging due to the intrinsically complex tree structure of the airway and the imbalance in branch sizes. While current deep learning methods focus on model architecture optimization, they underutilize anatomical priors such as the spatial correlation between pulmonary arteries and bronchi beyond geometric grading level III. To address this limitation, we propose dual-decoding segmentation network (DDS-Net) integrated with a pulmonary-bronchial extension generative adversarial network (PBE-GAN), which explicitly embeds artery-bronchus adjacency priors to enhance distal bronchial identification. Experimental results demonstrate state-of-the-art performance, achieving a Dice Similarity Coefficient (DSC) of 88.46%, Branch Detection Rate (BD) of 88.31%, and Tree Length Detection Rate (TD) of 84.93%, with significant improvements in detecting peripheral bronchi near pulmonary arteries. This study confirms that incorporating anatomical relationships substantially improves segmentation accuracy, particularly for fine structures. Future work should prioritize clinical validation through multi-center trials and explore integration with real-time surgical navigation systems, while extending similar anatomical synergy principles to other organ-specific segmentation tasks.
Keyword ：
Airway segmentation Airway segmentation Artery accompany Artery accompany Generative adversarial network Generative adversarial network Prior knowledge Prior knowledge
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| GB/T 7714 | Zhang, Zhen , Zhang, Wen , Huang, Liqin et al. Dual-phase airway segmentation: Enhancing distal bronchial identification with anatomical prior guidance [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2025 , 153 . |
| MLA | Zhang, Zhen et al. "Dual-phase airway segmentation: Enhancing distal bronchial identification with anatomical prior guidance" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 153 (2025) . |
| APA | Zhang, Zhen , Zhang, Wen , Huang, Liqin , Pan, Lin , Zheng, Shaohua , Liu, Zheng et al. Dual-phase airway segmentation: Enhancing distal bronchial identification with anatomical prior guidance . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2025 , 153 . |
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Neural networks have found widespread application in medical image registration, although they typically assume access to the entire training dataset during training. In clinical scenarios, medical images of various anatomical targets, such as the heart, brain, and liver, may be obtained successively with advancements in imaging technologies and diagnostic procedures. The accuracy of registration on a new target may degrade over time, as the registration models become outdated due to domain shifts occurring at unpredictable intervals. In this study, we introduce a deep registration model based on continual learning to mitigate the issue of catastrophic forgetting during training with continuous data streams. To enable continuous network training, we propose a dynamic memory system based on a density-based clustering algorithm to retain representative samples from the data stream. Training the registration network on these representative samples enhances its generalization capabilities to accommodate new targets within the data stream. We evaluated our approach using the CHAOS dataset, which comprises multiple targets, such as the liver, left kidney, and spleen, to simulate a data stream. The experimental findings illustrate that the proposed continual registration network achieves comparable performance to a model trained with full data visibility.
Keyword ：
continual learning continual learning Data models Data models dynamic memory dynamic memory Heuristic algorithms Heuristic algorithms Liver Liver Medical diagnostic imaging Medical diagnostic imaging Registration network Registration network Streams Streams Task analysis Task analysis Training Training
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| GB/T 7714 | Ding, Wangbin , Sun, Haoran , Pei, Chenhao et al. Multi-Organ Registration With Continual Learning [J]. | IEEE SIGNAL PROCESSING LETTERS , 2024 , 31 : 1204-1208 . |
| MLA | Ding, Wangbin et al. "Multi-Organ Registration With Continual Learning" . | IEEE SIGNAL PROCESSING LETTERS 31 (2024) : 1204-1208 . |
| APA | Ding, Wangbin , Sun, Haoran , Pei, Chenhao , Jia, Dengqiang , Huang, Liqin . Multi-Organ Registration With Continual Learning . | IEEE SIGNAL PROCESSING LETTERS , 2024 , 31 , 1204-1208 . |
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Cine imaging serves as a vital approach for non-invasive assessment of cardiac functional parameters. The imaging process of Cine cardiac MRI is inherently slow, necessitating the acquisition of data at multiple time points within each cardiac cycle to ensure adequate temporal resolution and motion information. Over prolonged data acquisition and during motion, Cine images can exhibit image degradation, leading to the occurrence of artifacts. Conventional image reconstruction methods often require expert knowledge for feature selection, which may result in information loss and suboptimal outcomes. In this paper, we employ a data-driven deep learning approach to address this issue. This approach utilizes supervised learning to compare data with different acceleration factors to full-sampled spatial domain data, training a context-aware network to reconstruct images with artifacts. In our model training strategy, we employ an adversarial approach to make the reconstructed images closer to ground truth. We incorporate loss functions based on adversarial principles and introduce image quality assessment as a constraint. Our context-aware model efficiently accomplishes artifact removal and image reconstruction tasks.
Keyword ：
Cine MRI Cine MRI Context Encoder Context Encoder Deep Learning Deep Learning Generative Adversarial Networks Generative Adversarial Networks Image Reconstruction Image Reconstruction
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| GB/T 7714 | Zhang, Weihua , Tang, Mengshi , Huang, Liqin et al. A Context-Encoders-Based Generative Adversarial Networks for Cine Magnetic Resonance Imaging Reconstruction [J]. | STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. REGULAR AND CMRXRECON CHALLENGE PAPERS, STACOM 2023 , 2024 , 14507 : 359-368 . |
| MLA | Zhang, Weihua et al. "A Context-Encoders-Based Generative Adversarial Networks for Cine Magnetic Resonance Imaging Reconstruction" . | STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. REGULAR AND CMRXRECON CHALLENGE PAPERS, STACOM 2023 14507 (2024) : 359-368 . |
| APA | Zhang, Weihua , Tang, Mengshi , Huang, Liqin , Li, Wei . A Context-Encoders-Based Generative Adversarial Networks for Cine Magnetic Resonance Imaging Reconstruction . | STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. REGULAR AND CMRXRECON CHALLENGE PAPERS, STACOM 2023 , 2024 , 14507 , 359-368 . |
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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.
Keyword ：
Domain adaptation Domain adaptation medical image segmentation medical image segmentation multi-source multi-source unsupervised learning unsupervised learning
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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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Recently, the nnU-Net network had achieved excellent performance in many medical image segmentation tasks. However, it also had some obvious problems, such as being able to only perform fully supervised tasks, excessive resource consumption in the predict. Therefore, in the abdominal multi-organ challenge of FLARE23, only incomplete labeled data was provided, and the size of them was too large, which made the original nnU-Net difficult to run. Based on this, we had designed a framework that utilized generated pseudo labels and two-stage segmentation for fast and effective prediction. Specifically, we designed three nnU-Net, one for generating high-quality pseudo labels for unlabeled data, the other for generating coarse segmentation to guide cropping, and the third for achieving effective segmentation. Our method achieved an average DSC score of 88.87% and 38.00% for the organs and lesions on the validation set and the average running time and area under GPU memory-time cure are 45 s and 3000 MB, respectively.
Keyword ：
Low Consumption Low Consumption Pseudo Label Pseudo Label Two-stage Two-stage
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| GB/T 7714 | Yang, Xinye , Zhang, Xuru , Yan, Xiaochao et al. Abdomen Multi-organ Segmentation Using Pseudo Labels and Two-Stage [J]. | FAST, LOW-RESOURCE, AND ACCURATE ORGAN AND PAN-CANCER SEGMENTATION IN ABDOMEN CT, FLARE 2023 , 2024 , 14544 : 41-53 . |
| MLA | Yang, Xinye et al. "Abdomen Multi-organ Segmentation Using Pseudo Labels and Two-Stage" . | FAST, LOW-RESOURCE, AND ACCURATE ORGAN AND PAN-CANCER SEGMENTATION IN ABDOMEN CT, FLARE 2023 14544 (2024) : 41-53 . |
| APA | Yang, Xinye , Zhang, Xuru , Yan, Xiaochao , Ding, Wangbin , Chen, Hao , Huang, Liqin . Abdomen Multi-organ Segmentation Using Pseudo Labels and Two-Stage . | FAST, LOW-RESOURCE, AND ACCURATE ORGAN AND PAN-CANCER SEGMENTATION IN ABDOMEN CT, FLARE 2023 , 2024 , 14544 , 41-53 . |
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Parkinson's disease (PD) is a serious neurological disease. Many studies have preseted regions of interest such as substantia nigra (SN) for PD detection from magnetic resonance imaging (MRI). However, the SN is not the only region with remarkable tissue changes in PD MRIs. Patients with Prodromal Parkinson's Disease usually present with non-motor symptoms, and the associated brain regions may show varying degrees of damage on imaging. Therefore, exploring PD-related regions from whole-brain MRI is essential. In this study, we proposed an interpretable PD detection framework, including PD classification and feature region visualization. Specifically, we constructed a 3D ResNet model that could detect PD from whole-brain MRIs and discover other brain regions related to PD through 3D Gradient-weighted Class Activation Mapping (Grad-CAM) and Unified Parkinson's Disease Rating Scale (UPDRS). We obtained T1-Weighted MRIs from the Parkinson's Progression Markers Initiative (PPMI) database. The average classification accuracy of the 5-fold cross-validation and held-out dataset reached 96.1% and 94.5%, respectively. In addition, we used the 3D Grad-CAM framework to extract the weight of the feature map and obtain visual interpretation. The heat map highlighted the regions that were crucial for PD classification and found significant differences between PD and HC in frontal lobe related to linguistic semantic disorders. The UPDRS scores of PD and HC on the linguistic semantic function items were also remarkably different. Combined with previous studies, this work verified the significance of the frontal lobe and proved that the correlation between the frontal lobe and the pathogenesis of PD was explanatory.
Keyword ：
3D ResNet 3D ResNet Frontal lobe Frontal lobe Grad-CAM Grad-CAM MRI MRI Parkinson's diseases Parkinson's diseases Semantics Semantics
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| GB/T 7714 | Yang, Mingjing , Huang, Xianbin , Huang, Liqin et al. Diagnosis of Parkinson's disease based on 3D ResNet: The frontal lobe is crucial [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2023 , 85 . |
| MLA | Yang, Mingjing et al. "Diagnosis of Parkinson's disease based on 3D ResNet: The frontal lobe is crucial" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 85 (2023) . |
| APA | Yang, Mingjing , Huang, Xianbin , Huang, Liqin , Cai, Guoen . Diagnosis of Parkinson's disease based on 3D ResNet: The frontal lobe is crucial . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2023 , 85 . |
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