Automatic surgical instrument segmentation of endoscopic images is a crucial building block of many computer-assistance applications for minimally invasive surgery. So far, state-of-the-art approaches completely rely on the availability of a ground-truth supervision signal, obtained via manual annotation, thus expensive to collect at large scale. In this paper, we present FUN-SIS, a Fully-UNsupervised approach for binary Surgical Instrument Segmentation. FUN-SIS trains a per-frame segmentation model on completely unlabelled endoscopic videos, by solely relying on implicit motion information and instrument shape-priors. We define shape-priors as realistic segmentation masks of the instruments, not necessarily coming from the same dataset/domain as the videos. The shape-priors can be collected in various and convenient ways, such as recycling existing annotations from other datasets. We leverage them as part of a novel generative-adversarial approach, allowing to perform unsupervised instrument segmentation of optical-flow images during training. We then use the obtained instrument masks as pseudo-labels in order to train a per-frame segmentation model; to this aim, we develop a learning-from-noisy-labels architecture, designed to extract a clean supervision signal from these pseudo-labels, leveraging their peculiar noise properties. We validate the proposed contributions on three surgical datasets, including the MICCAI 2017 EndoVis Robotic Instrument Segmentation Challenge dataset. The obtained fully-unsupervised results for surgical instrument segmentation are almost on par with the ones of fully-supervised state-of-the-art approaches. This suggests the tremendous potential of the proposed method to leverage the great amount of unlabelled data produced in the context of minimally invasive surgery.
Toward Real-Time Surgical Scene Segmentation via a Spike-Driven Video Transformer with Spike-Informed Pretraining
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；University of Alberta（阿尔伯塔大学）；School of Medicine, Yale University（耶鲁大学医学院）；Southern University of Science and Technology（南方科技大学）；Nanfang Hospital Southern Medical University（南方医科大学南华医院）；School of Biomedical Engineering, Shenzhen University（深圳大学生物医学工程学院）；Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology（深圳先进技术研究院计算机科学与控制工程学院）
Large-scale Self-supervised Video Foundation Model for Intelligent Surgery
The Hong Kong University of Science and Technology（香港科技大学）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University Hospital（海德堡大学医院）；The Hong Kong Polytechnic University（香港理工大学）；National University of Singapore（新加坡国立大学）；Tongji University（同济大学）；The Chinese University of Hong Kong（香港中文大学）；Heidelberg University（海德堡大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；Shenzhen-Hong Kong Collaborative Innovation Research Institute（深港协同创新研究院）
VLM-Guided Iterative Refinement for Surgical Image Segmentation with Foundation Models
School of Automation Science and Engineering, South China University of Technology, Guangzhou, China（自动化科学与工程学院，华南理工大学，广州，中国）；Vanderbilt University, Nashville, TN, USA（范德比尔特大学，纳什维尔，田纳西州，美国）；The Pennsylvania State University, University Park, PA , USA（宾夕法尼亚州立大学，大学园，宾夕法尼亚州，美国）；Peking University, Beijing , China（北京大学，北京，中国）；Virginia Commonwealth University, Richmond, VA, USA（弗吉尼亚共同wealth大学，里士满，弗吉尼亚州，美国）；University of California San Diego, San Diego, CA, USA（加州大学圣地亚哥分校，圣地亚哥，加利福尼亚州，美国）
SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation
School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；School of Software Engineering, South China University of Technology（华南理工大学软件工程学院）；Department of Data Science & AI, Monash University（莫纳什大学数据科学与人工智能系）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机科学学院）；Department of Computer Science, University of Rochester（罗切斯特大学计算机科学系）
本文提出 SurgicalPart-SAM (SP-SAM)，通过协同提示、跨模态编码器和部分到整体自适应融合与分层解码，将器械结构知识与 SAM 集成，解决手术器械分割中结构复杂和提示不灵活的问题，以最少的可调参数实现了最先进的性能。
Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery
Wellcome/EPSRC Centre for Interventional and Surgical Sciences（Wellcome/EPSRC介入与外科科学中心）；University College London（伦敦大学学院）；Xiamen University（厦门大学）；The University of Hong Kong（香港大学）；National University of Singapore（新加坡国立大学）
RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation Network
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
GroundedSurg: A Multi-Procedure Benchmark for Language-Conditioned Surgical Tool Segmentation
King Abdullah University of Science and Technology (KAUST)（卡奥尔大学科学与技术学院）；Thapar Institute of Engineering and Technology（塔帕尔工程与技术学院）；The University of Queensland（昆士兰大学）；Gaash Research Lab, National Institute of Technology Srinagar（加什研究实验室，锡纳加尔国家理工学院）
Pseudo-label Guided Cross-video Pixel Contrast for Robotic Surgical Scene Segmentation with Limited Annotations
The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University College London（伦敦大学学院）；Zhejiang Lab（之江实验室）
Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation
University College London（伦敦大学学院）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data
Information Technology University（信息技术大学）；University of Bristol（布里斯托尔大学）；Bristol Heart Institute（布里斯托尔心脏研究所）；University of the West of England（西英格兰大学）；Qatar University（卡塔尔大学）
