Medical image analysis最新文献

{"title":"PitVis-2023 challenge: Workflow recognition in videos of endoscopic pituitary surgery","authors":"Adrito Das ,&nbsp;Danyal Z. Khan ,&nbsp;Dimitrios Psychogyios ,&nbsp;Yitong Zhang ,&nbsp;John G. Hanrahan ,&nbsp;Francisco Vasconcelos ,&nbsp;You Pang ,&nbsp;Zhen Chen ,&nbsp;Jinlin Wu ,&nbsp;Xiaoyang Zou ,&nbsp;Guoyan Zheng ,&nbsp;Abdul Qayyum ,&nbsp;Moona Mazher ,&nbsp;Imran Razzak ,&nbsp;Tianbin Li ,&nbsp;Jin Ye ,&nbsp;Junjun He ,&nbsp;Szymon Płotka ,&nbsp;Joanna Kaleta ,&nbsp;Amine Yamlahi ,&nbsp;Sophia Bano","doi":"10.1016/j.media.2025.103716","DOIUrl":"10.1016/j.media.2025.103716","url":null,"abstract":"<div><div>The field of computer vision applied to videos of minimally invasive surgery is ever-growing. Workflow recognition pertains to the automated recognition of various aspects of a surgery, including: which surgical steps are performed; and which surgical instruments are used. This information can later be used to assist clinicians when learning the surgery or during live surgery. The Pituitary Vision (PitVis) 2023 Challenge tasks the community to step and instrument recognition in videos of endoscopic pituitary surgery. This is a particularly challenging task when compared to other minimally invasive surgeries due to: the smaller working space, which limits and distorts vision; and higher frequency of instrument and step switching, which requires more precise model predictions. Participants were provided with 25-videos, with results presented at the MICCAI-2023 conference as part of the Endoscopic Vision 2023 Challenge in Vancouver, Canada, on 08-Oct-2023. There were 18-submissions from 9-teams across 6-countries, using a variety of deep learning models. The top performing model for step recognition utilised a transformer based architecture, uniquely using an autoregressive decoder with a positional encoding input. The top performing model for instrument recognition utilised a spatial encoder followed by a temporal encoder, which uniquely used a 2-layer temporal architecture. In both cases, these models outperformed purely spatial based models, illustrating the importance of sequential and temporal information. This PitVis-2023 therefore demonstrates state-of-the-art computer vision models in minimally invasive surgery are transferable to a new dataset. Benchmark results are provided in the paper, and the dataset is publicly available at: <span><span>https://doi.org/10.5522/04/26531686</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"107 ","pages":"Article 103716"},"PeriodicalIF":11.8,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rethinking data imbalance in class incremental surgical instrument segmentation","authors":"Shifang Zhao ,&nbsp;Long Bai ,&nbsp;Kun Yuan ,&nbsp;Feng Li ,&nbsp;Jieming Yu ,&nbsp;Wenzhen Dong ,&nbsp;Guankun Wang ,&nbsp;Mobarakol Islam ,&nbsp;Nicolas Padoy ,&nbsp;Nassir Navab ,&nbsp;Hongliang Ren","doi":"10.1016/j.media.2025.103728","DOIUrl":"10.1016/j.media.2025.103728","url":null,"abstract":"<div><div>In surgical instrument segmentation, the increasing variety of instruments over time poses a significant challenge for existing neural networks, as they are unable to effectively learn such incremental tasks and suffer from catastrophic forgetting. When learning new data, the model experiences a sharp performance drop on previously learned data. Although several continual learning methods have been proposed for incremental understanding tasks in surgical scenarios, the issue of data imbalance often leads to a strong bias in the segmentation head, resulting in poor performance. Data imbalance can occur in two forms: (i) class imbalance between new and old data, and (ii) class imbalance within the same time point of data. Such imbalances often cause the dominant classes to take over the training process of continual semantic segmentation (CSS). To address this issue, we propose <strong>SurgCSS</strong>, a novel plug-and-play CSS framework for surgical instrument segmentation under data imbalance. Specifically, we generate realistic surgical backgrounds through inpainting and blend instrument foregrounds with the generated backgrounds in a class-aware manner to balance the data distribution in various scenarios. We further propose the Class Desensitization Loss by employing contrastive learning to correct edge biases caused by data imbalance. Moreover, we dynamically fuse the weight parameters of the old and new models to achieve a better trade-off between the biased and unbiased model weights. To investigate the data imbalance problem in surgical scenarios, we construct a new benchmark for surgical instrument CSS by integrating four public datasets: EndoVis 2017, EndoVis 2018, CholecSeg8k, and SAR-RAPR50. Extensive experiments demonstrate the effectiveness of the proposed framework, achieving significant performance improvement against existing baselines. Our method demonstrates excellent potential for clinical applications. The code is publicly available at <span><span>github.com/Zzsf11/SurgCSS</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"105 ","pages":"Article 103728"},"PeriodicalIF":11.8,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new dataset and versatile multi-task surgical workflow analysis framework for thoracoscopic mitral valvuloplasty","authors":"Meng Lan ,&nbsp;Weixin Si ,&nbsp;Xinjian Yan ,&nbsp;Xiaomeng Li","doi":"10.1016/j.media.2025.103724","DOIUrl":"10.1016/j.media.2025.103724","url":null,"abstract":"<div><div>Surgical Workflow Analysis (SWA) on videos is critical for AI-assisted intelligent surgery. Existing SWA methods primarily focus on laparoscopic surgeries, while research on complex thoracoscopy-assisted cardiac surgery remains largely unexplored. In this paper, we introduce <strong>TMVP-SurgVideo</strong>, the first SWA video dataset for thoracoscopic cardiac mitral valvuloplasty (TMVP). TMVP-SurgVideo comprises 57 independent long-form surgical videos and over 429K annotated frames, covering four key tasks, namely phase and instrument recognitions, and phase and instrument anticipations. To achieve a comprehensive SWA system for TMVP and overcome the limitations of current SWA methods, we propose <strong>SurgFormer</strong>, the first query-based Transformer framework that simultaneously performs recognition and anticipation of surgical phases and instruments. SurgFormer uses four low-dimensional learnable task embeddings to independently decode representation embeddings for the predictions of the four tasks. During the decoding process, an information interaction module that contains the intra-frame task-level information interaction layer and the inter-frame temporal correlation learning layer is devised to operate on the task embeddings, enabling the information collaboration between tasks within each frame and temporal correlation learning of each task across frames. Besides, SurgFormer’s unique architecture allows it to perform both offline and online inferences using a dynamic memory bank without model modification. Our proposed SurgFormer is evaluated on the TMVP-SurgVideo and existing Cholec80 datasets to demonstrate its effectiveness on SWA. The dataset and the code are available at <span><span>https://github.com/xmed-lab/SurgFormer</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"105 ","pages":"Article 103724"},"PeriodicalIF":11.8,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"POPAR: Patch Order Prediction and Appearance Recovery for self-supervised learning in chest radiography","authors":"Jiaxuan Pang ,&nbsp;Dongao Ma ,&nbsp;Ziyu Zhou ,&nbsp;Michael B. Gotway ,&nbsp;Jianming Liang","doi":"10.1016/j.media.2025.103720","DOIUrl":"10.1016/j.media.2025.103720","url":null,"abstract":"<div><div>Self-supervised learning (SSL) has proven effective in reducing the dependency on large annotated datasets while achieving state-of-the-art (SoTA) performance in computer vision. However, its adoption in medical imaging remains slow due to fundamental differences between photographic and medical images. To address this, we propose POPAR (Patch Order Prediction and Appearance Recovery), a novel SSL framework tailored for medical image analysis, particularly chest X-ray interpretation. POPAR introduces two key learning strategies: (1) Patch order prediction, which helps the model learn anatomical structures and spatial relationships by predicting the arrangement of shuffled patches, and (2) Patch appearance recovery, which reconstructs fine-grained details to enhance texture-based feature learning. Using a Swin Transformer backbone, POPAR is pretrained on a large-scale dataset and extensively evaluated across multiple tasks, outperforming both SSL and fully supervised SoTA models in classification, segmentation, anatomical understanding, bias robustness, and data efficiency. Our findings highlight POPAR’s scalability, strong generalization, and effectiveness in medical imaging applications. All code and models are available at <span><span>GitHub.com/JLiangLab/POPAR</span><svg><path></path></svg></span> (Version 2).</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"105 ","pages":"Article 103720"},"PeriodicalIF":10.7,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HELPNet: Hierarchical perturbations consistency and entropy-guided ensemble for scribble supervised medical image segmentation","authors":"Xiao Zhang ,&nbsp;Shaoxuan Wu ,&nbsp;Peilin Zhang ,&nbsp;Zhuo Jin ,&nbsp;Xiaosong Xiong ,&nbsp;Qirong Bu ,&nbsp;Jingkun Chen ,&nbsp;Jun Feng","doi":"10.1016/j.media.2025.103719","DOIUrl":"10.1016/j.media.2025.103719","url":null,"abstract":"<div><div>Creating fully annotated labels for medical image segmentation is prohibitively time-intensive and costly, emphasizing the necessity for innovative approaches that minimize reliance on detailed annotations. Scribble annotations offer a more cost-effective alternative, significantly reducing the expenses associated with full annotations. However, scribble annotations offer limited and imprecise information, failing to capture the detailed structural and boundary characteristics necessary for accurate organ delineation. To address these challenges, we propose HELPNet, a novel scribble-based weakly supervised segmentation framework, designed to bridge the gap between annotation efficiency and segmentation performance. HELPNet integrates three modules. The Hierarchical perturbations consistency (HPC) module enhances feature learning by employing density-controlled jigsaw perturbations across global, local, and focal views, enabling robust modeling of multi-scale structural representations. Building on this, the Entropy-guided pseudo-label (EGPL) module evaluates the confidence of segmentation predictions using entropy, generating high-quality pseudo-labels. Finally, the Structural prior refinement (SPR) module integrates connectivity analysis and image boundary prior to refine pseudo-label quality and enhance supervision. Experimental results on three public datasets ACDC, MSCMRseg, and CHAOS show that HELPNet significantly outperforms state-of-the-art methods for scribble-based weakly supervised segmentation and achieves performance comparable to fully supervised methods. The code is available at <span><span>https://github.com/IPMI-NWU/HELPNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"105 ","pages":"Article 103719"},"PeriodicalIF":10.7,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-modality image synthesis from TOF-MRA to CTA using diffusion-based models","authors":"Alexander Koch ,&nbsp;Orhun Utku Aydin ,&nbsp;Adam Hilbert ,&nbsp;Jana Rieger ,&nbsp;Satoru Tanioka ,&nbsp;Fujimaro Ishida ,&nbsp;Dietmar Frey","doi":"10.1016/j.media.2025.103722","DOIUrl":"10.1016/j.media.2025.103722","url":null,"abstract":"<div><div>Cerebrovascular disease often requires multiple imaging modalities for accurate diagnosis, treatment, and monitoring. Computed Tomography Angiography (CTA) and Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) are two common non-invasive angiography techniques, each with distinct strengths in accessibility, safety, and diagnostic accuracy. While CTA is more widely used in acute stroke due to its faster acquisition times and higher diagnostic accuracy, TOF-MRA is preferred for its safety, as it avoids radiation exposure and contrast agent-related health risks. Despite the predominant role of CTA in clinical workflows, there is a scarcity of open-source CTA data, limiting the research and development of AI models for tasks such as large vessel occlusion detection and aneurysm segmentation. This study explores diffusion-based image-to-image translation models to generate synthetic CTA images from TOF-MRA input. We demonstrate the modality conversion from TOF-MRA to CTA and show that diffusion models outperform a traditional U-Net-based approach. Our work compares different state-of-the-art diffusion architectures and samplers, offering recommendations for optimal model performance in this cross-modality translation task.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"105 ","pages":"Article 103722"},"PeriodicalIF":10.7,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FetalFlex: Anatomy-guided diffusion model for flexible control on fetal ultrasound image synthesis","authors":"Yaofei Duan ,&nbsp;Tao Tan ,&nbsp;Zhiyuan Zhu ,&nbsp;Yuhao Huang ,&nbsp;Yuanji Zhang ,&nbsp;Rui Gao ,&nbsp;Patrick Cheong-Iao Pang ,&nbsp;Xinru Gao ,&nbsp;Guowei Tao ,&nbsp;Xiang Cong ,&nbsp;Zhou Li ,&nbsp;Lianying Liang ,&nbsp;Guangzhi He ,&nbsp;Linliang Yin ,&nbsp;Xuedong Deng ,&nbsp;Xin Yang ,&nbsp;Dong Ni","doi":"10.1016/j.media.2025.103725","DOIUrl":"10.1016/j.media.2025.103725","url":null,"abstract":"<div><div>Fetal ultrasound (US) examinations require the acquisition of multiple planes, each providing unique diagnostic information to evaluate fetal development and screening for congenital anomalies. However, obtaining a thorough, multi-plane annotated fetal US dataset remains challenging, particularly for rare or complex anomalies owing to their low incidence and numerous subtypes. This poses difficulties in training novice radiologists and developing robust AI models, especially for detecting abnormal fetuses. In this study, we introduce a Flexible Fetal US image generation framework (FetalFlex) to address these challenges, which leverages anatomical structures and multimodal information to enable controllable synthesis of fetal US images across diverse planes. Specifically, FetalFlex incorporates a pre-alignment module to enhance controllability and introduces a repaint strategy to ensure consistent texture and appearance. Moreover, a two-stage adaptive sampling strategy is developed to progressively refine image quality from coarse to fine levels. We believe that FetalFlex is the first method capable of generating both in-distribution normal and out-of-distribution abnormal fetal US images, without requiring any abnormal data. Experiments on multi-center datasets demonstrate that FetalFlex achieved state-of-the-art performance across multiple image quality metrics. Comprehensive reader studies further confirms the close alignment of the generated results with expert visual assessments and clinical-level fidelity. Furthermore, synthetic images by FetalFlex significantly improve the performance of six typical deep models in downstream classification and anomaly detection tasks. Lastly, FetalFlex’s anatomy-level controllable generation offers a unique advantage for anomaly simulation and creating paired or counterfactual data at the pixel level. The demo is available at: <span><span>https://dyf1023.github.io/FetalFlex/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"105 ","pages":"Article 103725"},"PeriodicalIF":11.8,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ViFT: Visual field transformer for visual field testing via deep reinforcement learning","authors":"Shozo Saeki,&nbsp;Minoru Kawahara,&nbsp;Hirohisa Aman","doi":"10.1016/j.media.2025.103721","DOIUrl":"10.1016/j.media.2025.103721","url":null,"abstract":"<div><div>Visual field testing (perimetry) quantifies a patient’s visual field sensitivity to diagnosis and follow-up on their visual impairments. Visual field testing would require the patients to concentrate on the test for a long time. However, a longer testing time makes patients more exhausted and leads to a decrease in testing accuracy. Thus, it is helpful to develop a well-designed strategy to finish the testing more quickly while maintaining high accuracy. This paper proposes the visual field transformer (ViFT) for visual field testing with deep reinforcement learning. This study contributes to the following four: (1) ViFT can fully control the visual field testing process. (2) ViFT learns the relationships of visual field locations without any pre-defined information. (3) ViFT learning process can consider the patient perception uncertainty. (4) ViFT achieves the same or higher accuracy than the other strategies, and half as test time as the other strategies. Our experiments demonstrate the ViFT efficiency on the 24-2 test pattern compared with other strategies.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"105 ","pages":"Article 103721"},"PeriodicalIF":10.7,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning homeomorphic image registration via conformal-invariant hyperelastic regularisation","authors":"Jing Zou ,&nbsp;Noémie Debroux ,&nbsp;Lihao Liu ,&nbsp;Jing Qin ,&nbsp;Carola-Bibiane Schönlieb ,&nbsp;Angelica I. Aviles-Rivero","doi":"10.1016/j.media.2025.103712","DOIUrl":"10.1016/j.media.2025.103712","url":null,"abstract":"<div><div>Deformable image registration is a fundamental task in medical image analysis and plays a crucial role in a wide range of clinical applications. Recently, deep learning-based approaches have been widely studied for deformable medical image registration and achieved promising results. However, existing deep learning image registration techniques do not theoretically guarantee topology-preserving transformations. This is a key property to preserve anatomical structures and achieve plausible transformations that can be used in real clinical settings. We propose a novel framework for deformable image registration. Firstly, we introduce a novel regulariser based on conformal-invariant properties in a nonlinear elasticity setting. Our regulariser enforces the deformation field to be mooth, invertible and orientation-preserving. More importantly, we strictly guarantee topology preservation yielding to a clinical meaningful registration. Secondly, we boost the performance of our regulariser through coordinate MLPs, where one can view the to-be-registered images as continuously differentiable entities. We demonstrate, through numerical and visual experiments, that our framework is able to outperform current techniques for image registration.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"105 ","pages":"Article 103712"},"PeriodicalIF":10.7,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ROP lesion segmentation via sequence coding and block balancing","authors":"Xiping Jia ,&nbsp;Jianying Qiu ,&nbsp;Dong Nie ,&nbsp;Tian Liu","doi":"10.1016/j.media.2025.103723","DOIUrl":"10.1016/j.media.2025.103723","url":null,"abstract":"<div><div>Retinopathy of prematurity (ROP) is a potentially blinding retinal disease that often affects low birth weight premature infants. Lesion detection and recognition are crucial for ROP diagnosis and clinical treatment. However, this task poses challenges for both ophthalmologists and computer-based systems due to the small size and subtle nature of many ROP lesions. To address these challenges, we present a Sequence encoding and Block balancing-based Segmentation Network (SeBSNet), which incorporates domain knowledge coding, sequence coding learning (SCL), and block-weighted balancing (BWB) techniques into the segmentation of ROP lesions. The experimental results demonstrate that SeBSNet outperforms existing state-of-the-art methods in the segmentation of ROP lesions, with average ROC_AUC, PR_AUC, and Dice scores of 98.84%, 71.90%, and 66.88%, respectively. Furthermore, the integration of the proposed techniques into ROP classification networks as an enhancing module leads to considerable improvements in classification performance.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"105 ","pages":"Article 103723"},"PeriodicalIF":10.7,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}