Surgical skill assessment is important for surgery training and quality control. Prior works on this task largely focus on basic surgical tasks such as suturing and knot tying performed in simulation settings. In contrast, surgical skill assessment is studied in this paper on a real clinical dataset, which consists of fifty-seven in-vivo laparoscopic surgeries and corresponding skill scores annotated by six surgeons. From analyses on this dataset, the clearness of operating field (COF) is identified as a good proxy for overall surgical skills, given its strong correlation with overall skills and high inter-annotator consistency. Then an objective and automated framework based on neural network is proposed to predict surgical skills through the proxy of COF. The neural network is jointly trained with a supervised regression loss and an unsupervised rank loss. In experiments, the proposed method achieves 0.55 Spearman's correlation with the ground truth of overall technical skill, which is even comparable with the human performance of junior surgeons.
Bridging the Ex-Vivo to In-Vivo Gap: Synthetic Priors for Monocular Depth Estimation in Specular Surgical Environments
RoCAL, Rochester Institute of Technology（罗切斯特理工学院RoCAL实验室）；Department of Otolaryngology Head and Neck Surgery, University of Rochester Medical Center（罗切斯特大学医学中心耳鼻喉科与头颈外科部门）；University of Washington, Department of Otolaryngology–Head and Neck Surgery（华盛顿大学耳鼻喉科与头颈外科部门）
Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy
Politecnico di Milano（米兰理工大学）；Istituto Italiano di Tecnologia（意大利技术研究院）；German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；Heidelberg University Hospital（海德堡大学医院）
SLIMBRAIN: Augmented Reality Real-Time Acquisition and Processing System For Hyperspectral Classification Mapping with Depth Information for In-Vivo Surgical Procedures
Research Center on Software Technologies and Multimedia Systems, Universidad Politécnica de Madrid（马德里理工大学软件技术与多媒体系统研究中心）；Department of Neurosurgery, Hospital Universitario 12 de Octubre, Universidad Complutense de Madrid（马德里康普顿斯大学十月十二日大学医院神经外科）
Real-Time Segmentation of Non-Rigid Surgical Tools based on Deep Learning and Tracking
University College London（伦敦大学学院）；Katholieke Universiteit Leuven（荷语天主教鲁汶大学）；University College London Hospitals（伦敦大学学院医院）；Universitair Ziekenhuis Leuven（鲁汶大学医院）
Acquiring Submillimeter-Accurate Multi-Task Vision Datasets for Computer-Assisted Orthopedic Surgery
University Hospital Balgrist, University of Zurich（苏黎世大学医院巴尔格里斯分院、苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；Institut de Recherche en Informatique de Toulouse（图卢兹计算机科学研究所）
Bridging the Markerless Tracking Validation Gap: Intraoperative X-ray Workflow Integration
The Hamlyn Centre for Robotic Surgery, Imperial College London（伦敦帝国理工学院哈姆林外科机器人中心）；Imperial College Healthcare NHS Trust（帝国理工学院医疗保健 NHS 信托基金）；Stanford University School of Medicine（斯坦福大学医学院）
SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge
Taiwan AI Academy（台湾人工智能学院）；National Taiwan University（台湾大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Electronic Science and Technology of China（电子科技大学）
SERV-CT: A disparity dataset from CT for validation of endoscopic 3D reconstruction
Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), UCL（伦敦大学学院威康/EPSRC介入与外科科学中心）；National Center for Tumor Diseases (NCT) Dresden（德累斯顿国家肿瘤疾病中心）；German Cancer Research Center (DKFZ)（德国癌症研究中心）
在计算机视觉中，参考数据集在促进立体重建中的算法开发方面已经非常成功。手术场景会产生特定的问题，包括缺乏清晰的角落特征，高度镜面反射的表面以及血液和烟雾的存在。已经使用CT和覆盖内窥镜视场的相对小的区域的体模图像或生物组织样本产生了公开可用的数据集。我们提出了一个基于CT的立体内窥镜重建验证数据集（SERV-CT）。将两个{ \it ex vivo}小型猪完整躯干尸体放置在内窥镜视野内，内窥镜和靶解剖结构在CT扫描中可见。内窥镜的方向手动对准立体视图。计算每个样本的8个立体对的参考视差和遮挡。对于第二个样品，采集RGB表面以帮助对准光滑的无特征表面。重复手动对准显示RMS视差精度约为2像素，深度精度约为2 mm。参考数据集包括具有对应校准、视差、深度和遮挡的内窥镜图像对，覆盖大部分内窥镜图像和一系列组织类型。包括平滑的镜面反射表面和具有显著深度变化的图像。我们评估了各种立体声算法的性能，从网上可用的存储库。算法之间存在显著差异，突出了外科内窥镜图像的一些挑战。SERV-CT数据集为手术应用提供了易于使用的立体验证，具有平滑的参考视差和深度，覆盖了大多数内窥镜图像。这很好地补充了现有的资源，我们希望将有助于外科内窥镜解剖重建算法的发展。
