[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3486":3,"related-3486":67},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":39,"slug":40,"view_count":41,"doi":42,"paper":43,"created_at":66},3486,"starU‐Net: An enhanced U‐Net architecture with star operation and multi‐view fusion for improved vessel segmentation","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fviw2.70197","Abstract Accurate vessel segmentation is crucial for diagnosing vascular diseases and supporting research in developmental biology. However, existing methods struggle to preserve fine capillaries and the topological continuity of vascular structures. In this study, we aim to develop a novel deep learning architecture that specializes in thin vessel segmentation for improved continuity. We propose starU‐Net, a four‐layer encoder‒decoder framework for vessel segmentation. It integrates three components in a problem‐driven design: (1) an enhanced feature extraction module leveraging a “star operation” to enable multiplicative feature interaction, (2) a shallow network design to minimize spatial degradation, and (3) a multi‐view feature fusion module using dynamic snake convolution to capture continuous tubular structures from multiple orientations. The model was trained and evaluated on three public retinal datasets and a novel, self‐constructed chick embryo dataset. Against CNN‐based, transformer‐based, and foundation‐model baselines, starU‐Net obtained the highest sensitivity, F 1 , area under the curve, and centreline Dice on all three public retinal datasets, at the cost of slightly lower specificity. The improvement in centreline Dice provides quantitative support for the reduction in thin vessel discontinuity. On the chick embryo dataset, whose vessels are markedly wider, the proposed starU‐Net was competitive but not leading. In summary, starU‐Net is an architecture that combines star operation‐based feature extraction with multi‐view tubular feature modeling to improve vascular segmentation. The proposed framework demonstrates strong performance in both clinical retinal imaging datasets and self‐constructed chick embryo angiogenesis data, serving as a promising computational tool for clinical decision support, biomedical research, and large‐scale vascular phenotyping in biomedical applications.","摘要　精确的血管分割对于诊断血管疾病和支持发育生物学研究至关重要。然而，现有方法难以保留细小毛细血管及血管结构的拓扑连续性。本研究旨在开发一种新型深度学习架构，专门针对细血管分割以改善其连续性。我们提出starU‐Net，一种用于血管分割的四层编码器‐解码器框架。该框架以问题驱动设计整合了三个组件：(1) 利用“星操作”实现乘性特征交互的增强特征提取模块，(2) 最小化空间退化的浅层网络设计，(3) 使用动态蛇形卷积从多方向捕获连续管状结构的多视角特征融合模块。该模型在三个公开视网膜数据集和一个新构建的鸡胚数据集上进行了训练和评估。与基于CNN、基于Transformer及基础模型的基线方法相比，starU‐Net在三个公开视网膜数据集上均获得了最高的灵敏度、F1值、曲线下面积和中心线Dice，代价是特异性略低。中心线Dice的提升为细血管不连续性的减少提供了定量支持。在血管明显更宽的鸡胚数据集上，所提出的starU‐Net具有竞争力但并非领先。总之，starU‐Net是一种结合基于星操作的特征提取与多视角管状特征建模以改进血管分割的架构。所提出的框架在临床视网膜成像数据集和自构建的鸡胚血管生成数据中均展现出强大性能，可作为临床决策支持、生物医学研究及生物医学应用中大规模血管表型分析的有前景的计算工具。",null,"Open Research (University of Surrey)","2026-09-22T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦医学血管分割的深度学习架构，与三农、农业信息化、智慧农业等主题无关，不予入选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":38},[28],{"code":29,"score":30,"supported":31,"title":32,"abstract":33,"tags":37},"I2",9,true,[],[34,35,36],"深度学习","deep learning","decision support",[],"农业相关性需要复核",[],"starU‐Net:AnenhancedU‐Netarchitecturewit-3486",3,"10.1002\u002Fviw2.70197",{"doi":42,"openalex_id":44,"authors":45,"venue":10,"cited_by_count":15,"oa_url":57,"card":58,"direction":64,"ingested_from":65},"W7213971471",[46,48,51,54],{"name":47,"orcid":9},"Mengwei Bai",{"name":49,"orcid":50},"Tong Li","https:\u002F\u002Forcid.org\u002F0000-0003-1556-1241",{"name":52,"orcid":53},"Jing Lin","https:\u002F\u002Forcid.org\u002F0000-0001-9865-2098",{"name":55,"orcid":56},"Peng Fei Huang","https:\u002F\u002Forcid.org\u002F0000-0003-3651-7813","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fviw2.70197",{"tldr":59,"method":60,"finding":61,"direction":62,"opportunity":63},"提出starU-Net，用星操作和多视角融合提升血管分割连续性。","四层编解码器，星操作特征提取，动态蛇卷积多视角融合，视网膜和鸡胚数据。","在三个视网膜数据集上敏感度、F1、AUC和中心线Dice最高，细血管连续性改善。","农业人工智能与决策模型","可将该细管状结构分割架构迁移至农业场景，如作物根系、叶脉或灌溉管道提取。","农业遥感与作物表型","openalex","2026-09-25T23:30:22.913090Z",{"total":15,"page":68,"page_size":68,"items":69},1,[]]