Producing manual, pixel-accurate, image segmentation labels is tedious and time-consuming. This is often a rate-limiting factor when large amounts of labeled images are required, such as for training deep convolutional networks for instrument-background segmentation in surgical scenes. No large datasets comparable to industry standards in the computer vision community are available for this task. To circumvent this problem, we propose to automate the creation of a realistic training dataset by exploiting techniques stemming from special effects and harnessing them to target training performance rather than visual appeal. Foreground data is captured by placing sample surgical instruments over a chroma key (a.k.a. green screen) in a controlled environment, thereby making extraction of the relevant image segment straightforward. Multiple lighting conditions and viewpoints can be captured and introduced in the simulation by moving the instruments and camera and modulating the light source. Background data is captured by collecting videos that do not contain instruments. In the absence of pre-existing instrument-free background videos, minimal labeling effort is required, just to select frames that do not contain surgical instruments from videos of surgical interventions freely available online. We compare different methods to blend instruments over tissue and propose a novel data augmentation approach that takes advantage of the plurality of options. We show that by training a vanilla U-Net on semi-synthetic data only and applying a simple post-processing, we are able to match the results of the same network trained on a publicly available manually labeled real dataset.
Object compositing based on 2D images is a challenging problem since it typically involves multiple processing stages such as color harmonization, geometry correction and shadow generation to generate realistic results. Furthermore, annotating training data pairs for compositing requires substantial manual effort from professionals, and is hardly scalable. Thus, with the recent advances in generative models, in this work, we propose a self-supervised framework for object compositing by leveraging the power of conditional diffusion models. Our framework can hollistically address the object comp…
ETH Zurich（苏黎世联邦理工学院）；Disney Research Zurich（苏黎世迪士尼研究院）
DuctTake is a system designed to enable practical compositing of multiple takes of a scene into a single video. Current industry solutions are based around object segmentation, a hard problem that requires extensive manual input and cleanup, making compositing an expensive part of the film-making process. Our method instead composites shots together by finding optimal spatiotemporal seams using motion-compensated 3D graph cuts through the video volume. We describe in detail the required components, decisions, and new techniques that together make a usable, interactive tool for compositing HD v…
Machine Vision-Based Surgical Lighting System:Design and Implementation
Faculty of Electrical Engineering（电气工程学院）；Iran University of Science and Technology（伊朗科学技术大学）；General Surgery Department（普通外科部门）；WWL NHS Trust（WWL国家健康服务信托基金）；Neurology Department（神经科部门）；Imam Khomeini Hospital（伊玛目·霍梅尼医院）
本文设计并实现基于 YOLOv11 的机器视觉手术照明系统，通过识别蓝色标记并控制双伺服电机自动引导 LED 光源，验证集 mAP@50 达 96.7%，可减轻医生负担并提升照明一致性。
Surgical Anatomy Recognition with Context Learning using Foundation Representations
Department of Biomedical Engineering, Medical Image Analysis, Eindhoven University of Technology（埃因霍温理工大学，生物医学工程系，医学图像分析）；Department of Electrical Engineering, Architectures for Reliable Image Analysis, Eindhoven University of Technology（埃因霍温理工大学，电气工程系，可靠图像分析架构）；Department of Surgery, University Medical Center Utrecht（乌得勒支大学医学中心，外科）；Department of Oncological Urology, University Medical Center Utrecht（乌得勒支大学医学中心，肿瘤泌尿外科）；Department of Urology, Catharina Hospital（凯瑟琳娜医院，泌尿外科）；Department of Electrical Engineering, Mobile Perception Systems Lab, Eindhoven University of Technology（埃因霍温理工大学，电气工程系，移动感知系统实验室）
Image compositing is a task of combining regions from different images to compose a new image. A common use case is background replacement of portrait images. To obtain high quality composites, professionals typically manually perform multiple editing steps such as segmentation, matting and foreground color decontamination, which is very time consuming even with sophisticated photo editing tools. In this paper, we propose a new method which can automatically generate high-quality image compositing without any user input. Our method can be trained end-to-end to optimize exploitation of contextu…
Manual labeling of gestures in robot-assisted surgery is labor intensive, prone to errors, and requires expertise or training. We propose a method for automated and explainable generation of gesture transcripts that leverages the abundance of data for image segmentation. Surgical context is detected using segmentation masks by examining the distances and intersections between the tools and objects. Next, context labels are translated into gesture transcripts using knowledge-based Finite State Machine (FSM) and data-driven Long Short Term Memory (LSTM) models. We evaluate the performance of eac…
University of Maryland, College Park（马里兰大学帕克分校）；Intuitive Surgical, Inc（直观外科公司）
Semantic image segmentation plays an important role in modeling patient-specific anatomy. We propose a convolution neural network, called Kid-Net, along with a training schema to segment kidney vessels: artery, vein and collecting system. Such segmentation is vital during the surgical planning phase in which medical decisions are made before surgical incision. Our main contribution is developing a training schema that handles unbalanced data, reduces false positives and enables high-resolution segmentation with a limited memory budget. These objectives are attained using dynamic weighting, ran…
In natural image matting, the goal is to estimate the opacity of the foreground object in the image. This opacity controls the way the foreground and background is blended in transparent regions. In recent years, advances in deep learning have led to many natural image matting algorithms that have achieved outstanding performance in a fully automatic manner. However, most of these algorithms only predict the alpha matte from the image, which is not sufficient to create high-quality compositions. Further, it is not possible to manually interact with these algorithms in any way except by directl…
Deep-Learning-Assisted Analysis of Cataract Surgery Videos
Alpen-Adria-Universität Klagenfurt（阿尔卑斯-亚得里亚大学克拉根福分校）；University of Applied Sciences Western Switzerland（瑞士西部应用科学大学）；University of Bern（伯尔尼大学）；University of Geneva（日内瓦大学）
ASI-Seg: Audio-Driven Surgical Instrument Segmentation with Surgeon Intention Understanding
Centre for Artificial Intelligence and Robotics (CAIR), Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；Chinese University of Hong Kong（香港中文大学）
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；Weill Cornell Graduate School for Medical Sciences（威尔康奈尔医学研究生院）
Pathologic analysis of surgical excision specimens for breast carcinoma is important to evaluate the completeness of surgical excision and has implications for future treatment. This analysis is performed manually by pathologists reviewing histologic slides prepared from formalin-fixed tissue. In this paper, we present Deep Multi-Magnification Network trained by partial annotation for automated multi-class tissue segmentation by a set of patches from multiple magnifications in digitized whole slide images. Our proposed architecture with multi-encoder, multi-decoder, and multi-concatenation out…
SegCol Challenge: Semantic Segmentation for Tools and Fold Edges in Colonoscopy data
UCL Hawkes Institute, University College London（伦敦大学学院霍克斯学院）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）；Medtronic（美敦力）；Department of Computer Science, University Liverpool（利物浦大学计算机科学系）
Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔兰根-纽伦堡大学）；Siemens Healthcare GmbH（西门子医疗保健有限公司）；Hochschule Furtwangen（富特旺根应用技术大学）；BG Trauma Center Ludwigshafen（路德维希港BG创伤中心）
X-ray based measurement and guidance are commonly used tools in orthopaedic surgery to facilitate a minimally invasive workflow. Typically, a surgical planning is first performed using knowledge of bone morphology and anatomical landmarks. Information about bone location then serves as a prior for registration during overlay of the planning on intra-operative X-ray images. Performing these steps manually however is prone to intra-rater/inter-rater variability and increases task complexity for the surgeon. To remedy these issues, we propose an automatic framework for planning and subsequent ove…
University College London（伦敦大学学院）；Adobe Research（奥多比研究院）
Patterns, which are collections of elements arranged in regular or near-regular arrangements, are an important graphic art form and widely used due to their elegant simplicity and aesthetic appeal. When a pattern is encoded as a flat image without the underlying structure, manually editing the pattern is tedious and challenging as one has to both preserve the individual element shapes and their original relative arrangements. State-of-the-art deep learning frameworks that operate at the pixel level are unsuitable for manipulating such patterns. Specifically, these methods can easily disturb th…
CataractSAM-2: A Domain-Adapted Model for Anterior Segment Surgery Segmentation and Scalable Ground-Truth Annotation
Thomas Jefferson High School for Science and Technology（托马斯·杰弗逊高中科学与技术学校）；Mass Eye and Ear, Harvard Medical School（麻省眼耳研究所，哈佛医学院）；Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Mass Eye and Ear, Harvard Medical School（哈佛眼科学人工智能实验室，麻省眼耳研究所的谢普恩斯眼研究学院，哈佛医学院）
Hybrid Synthetic Data Generation with Domain Randomization Enables Zero-Shot Vision-Based Part Inspection Under Extreme Class Imbalance
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA；Materials \& Manufacturing Systems Research Lab, General Motors, Warren, MI 48092, USA
The Hong Kong University of Science and Technology（香港科技大学）；Beihang University（北京航空航天大学）；Beihang Hangzhou Innovation Institute Yuhang（北航杭州创新研究院（余杭））
Surgical phase recognition is a fundamental task in computer-assisted surgery systems. Most existing works are under the supervision of expensive and time-consuming full annotations, which require the surgeons to repeat watching videos to find the precise start and end time for a surgical phase. In this paper, we introduce timestamp supervision for surgical phase recognition to train the models with timestamp annotations, where the surgeons are asked to identify only a single timestamp within the temporal boundary of a phase. This annotation can significantly reduce the manual annotation cost …
ICube, University of Strasbourg, CNRS, IHU Strasbourg（斯特拉斯堡大学ICube研究所、法国国家科学研究中心、斯特拉斯堡大学医院研究所）；University Hospital of Strasbourg, IRCAD, IHU Strasbourg（斯特拉斯堡大学医院、斯特拉斯堡 IRCAD 研究所、斯特拉斯堡大学医院研究所）
Real-time algorithms for automatically recognizing surgical phases are needed to develop systems that can provide assistance to surgeons, enable better management of operating room (OR) resources and consequently improve safety within the OR. State-of-the-art surgical phase recognition algorithms using laparoscopic videos are based on fully supervised training. This limits their potential for widespread application, since creation of manual annotations is an expensive process considering the numerous types of existing surgeries and the vast amount of laparoscopic videos available. In this work…
Peking University（北京大学）；Peking University Cancer Hospital（北京大学肿瘤医院）；Deepwise AI Lab（深睿医疗人工智能实验室）
Surgical skills have a great influence on surgical safety and patients' well-being. Traditional assessment of surgical skills involves strenuous manual efforts, which lacks efficiency and repeatability. Therefore, we attempt to automatically predict how well the surgery is performed using the surgical video. In this paper, a unified multi-path framework for automatic surgical skill assessment is proposed, which takes care of multiple composing aspects of surgical skills, including surgical tool usage, intraoperative event pattern, and other skill proxies. The dependency relationships among the…
Robot-assisted surgery is an established clinical practice. The automatic identification of surgical actions is needed for a range of applications, including performance assessment of trainees and surgical process modeling for autonomous execution and monitoring. However, supervised action identification is not feasible, due to the burden of manually annotating recordings of potentially complex and long surgical executions. Moreover, often few example executions of a surgical procedure can be recorded. This paper proposes a novel fast algorithm for unsupervised identification of surgical actio…
IRIMAS（IRIMAS研究所）；Université de Haute-Alsace（上阿尔萨斯大学）
The need for automatic surgical skills assessment is increasing, especially because manual feedback from senior surgeons observing junior surgeons is prone to subjectivity and time consuming. Thus, automating surgical skills evaluation is a very important step towards improving surgical practice. In this paper, we designed a Convolutional Neural Network (CNN) to evaluate surgeon skills by extracting patterns in the surgeon motions performed in robotic surgery. The proposed method is validated on the JIGSAWS dataset and achieved very competitive results with 100% accuracy on the suturing and ne…
Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised Data
Department of Computer Science and Engineering, University at Buffalo（计算机科学与工程系，布法罗大学）；Department of Surgery, University at Buffalo（外科系，布法罗大学）；Department of Computer Science and Engineering, IIT, Jodhpur（计算机科学与工程系，印度理工学院，乔杜尔）；Department of Computer Science and Engineering, University of Notre Dame（计算机科学与工程系，圣母大学）
Five billion people in the world lack access to quality surgical care. Surgeon skill varies dramatically, and many surgical patients suffer complications and avoidable harm. Improving surgical training and feedback would help to reduce the rate of complications, half of which have been shown to be preventable. To do this, it is essential to assess operative skill, a process that currently requires experts and is manual, time consuming, and subjective. In this work, we introduce an approach to automatically assess surgeon performance by tracking and analyzing tool movements in surgical videos, …
University Hospital of Strasbourg（斯特拉斯堡大学医院）；IRCAD（斯特拉斯堡 IRCAD 研究所）；IHU Strasbourg（斯特拉斯堡大学医院研究所）；ICube（斯特拉斯堡大学 ICube 实验室）；University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）
Vision algorithms capable of interpreting scenes from a real-time video stream are necessary for computer-assisted surgery systems to achieve context-aware behavior. In laparoscopic procedures one particular algorithm needed for such systems is the identification of surgical phases, for which the current state of the art is a model based on a CNN-LSTM. A number of previous works using models of this kind have trained them in a fully supervised manner, requiring a fully annotated dataset. Instead, our work confronts the problem of learning surgical phase recognition in scenarios presenting scar…
Automated Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy from DWI Data
Efi Arazi School of Computer Science, Reichman University（赖希曼大学埃菲·阿拉齐计算机科学学院）；Faculty of Biomedical Engineering, Technion – Israel Institute of Technology（以色列理工学院生物医学工程学院）
University of Zagreb（萨格勒布大学）；University of Twente（特文特大学）；University of Groningen（格罗宁根大学）；Radboud University Medical Center（拉德堡德大学医学中心）；Canisius-Wilhelmina Hospital（卡尼修斯-威廉明娜医院）
UCL（伦敦大学学院）；Università di Verona（维罗纳大学）；Politecnico di Milano（米兰理工大学）
Kinematic trajectories recorded from surgical robots contain information about surgical gestures and potentially encode cues about surgeon's skill levels. Automatic segmentation of these trajectories into meaningful action units could help to develop new metrics for surgical skill assessment as well as to simplify surgical automation. State-of-the-art methods for action recognition relied on manual labelling of large datasets, which is time consuming and error prone. Unsupervised methods have been developed to overcome these limitations. However, they often rely on tedious parameter tuning and…
NELVT, Department of Computer Science, Peking University（北京大学计算机科学技术系数字视频编解码技术国家工程实验室）；Deepwise AI Lab（深睿医疗人工智能实验室）；Center on Frontiers of Computing Studies, Peking University（北京大学前沿计算研究中心）；Advanced Institute of Information Technology, Peking University（北京大学信息技术高等研究院）；Peking University Cancer Hospital（北京大学肿瘤医院）
Surgical instrument segmentation is a key component in developing context-aware operating rooms. Existing works on this task heavily rely on the supervision of a large amount of labeled data, which involve laborious and expensive human efforts. In contrast, a more affordable unsupervised approach is developed in this paper. To train our model, we first generate anchors as pseudo labels for instruments and background tissues respectively by fusing coarse handcrafted cues. Then a semantic diffusion loss is proposed to resolve the ambiguity in the generated anchors via the feature correlation bet…
One Patient's Annotation is Another One's Initialization: Towards Zero-Shot Surgical Video Segmentation with Cross-Patient Initialization
Center for Biosystems and Biotech Data Science, Ghent University（根特大学生物系统与生物技术数据科学中心）；Ghent University Global Campus（根特大学全球校区）；Ghent University Hospital（根特大学医院）；Department of Human Structure and Repair, Ghent University（根特大学人类结构与修复系）；Department of HPB Surgery & Liver Transplantation, Ghent University Hospital（根特大学医院肝胆胰外科与肝移植科）；Department of Applied Mathematics, Computer Science and Statistics, Ghent University（根特大学应用数学、计算机科学与统计系）
TEMSET-24K: Densely Annotated Dataset for Indexing Multipart Endoscopic Videos using Surgical Timeline Segmentation
Birmingham City University（伯明翰城市大学）；University Hospitals Birmingham（伯明翰大学医院）；University of Birmingham（伯明翰大学）；University of the West of England（西英格兰大学）；University of Hertfordshire（赫特福德大学）；University of Bristol（布里斯托大学）；Qatar University（卡塔尔大学）
National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯医学院暨大学医院）
Purpose: A profound education of novice surgeons is crucial to ensure that surgical interventions are effective and safe. One important aspect is the teaching of technical skills for minimally invasive or robot-assisted procedures. This includes the objective and preferably automatic assessment of surgical skill. Recent studies presented good results for automatic, objective skill evaluation by collecting and analyzing motion data such as trajectories of surgical instruments. However, obtaining the motion data generally requires additional equipment for instrument tracking or the availability …
SurgNavAR: An Augmented Reality Surgical Navigation Framework for Optical See-Through Head Mounted Displays
Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC（鹿特丹伊拉斯姆斯大学医学中心放射与核医学系鹿特丹生物医学成像组）；Department of Oral and Maxillofacial Surgery, Erasmus MC（鹿特丹伊拉斯姆斯大学医学中心口腔颌面外科）；University of Groningen（格罗宁根大学）；Department of High Performance Compute & Visualization, SURF bv（SURF bv高性能计算与可视化部）；Department of Neurosurgery, Erasmus MC（鹿特丹伊拉斯姆斯大学医学中心神经外科）；Trauma Research Unit, Department of Surgery, Erasmus MC（鹿特丹伊拉斯姆斯大学医学中心外科创伤研究单元）
Johnson & Johnson（强生公司）；Johnson & Johnson Medical GmbH（强生医疗有限公司）
Mastering the technical skills required to perform surgery is an extremely challenging task. Video-based assessment allows surgeons to receive feedback on their technical skills to facilitate learning and development. Currently, this feedback comes primarily from manual video review, which is time-intensive and limits the feasibility of tracking a surgeon's progress over many cases. In this work, we introduce a motion-based approach to automatically assess surgical skills from surgical case video feed. The proposed pipeline first tracks surgical tools reliably to create motion trajectories and…
Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS)（惠康/工程与物理科学研究委员会介入与外科科学中心（WEISS））；University College London（伦敦大学学院）
Surgical gesture recognition is important for surgical data science and computer-aided intervention. Even with robotic kinematic information, automatically segmenting surgical steps presents numerous challenges because surgical demonstrations are characterized by high variability in style, duration and order of actions. In order to extract discriminative features from the kinematic signals and boost recognition accuracy, we propose a multi-task recurrent neural network for simultaneous recognition of surgical gestures and estimation of a novel formulation of surgical task progress. To show the…
University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）；IHU Strasbourg（斯特拉斯堡大学医院）；Politecnico di Milano（米兰理工大学）
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 an…
College of Computing, Georgia Institute of Technology（佐治亚理工学院计算学院）
Purpose: Manual feedback in basic RMIS training can consume a significant amount of time from expert surgeons' schedule and is prone to subjectivity. While VR-based training tasks can generate automated score reports, there is no mechanism of generating automated feedback for surgeons performing basic surgical tasks in RMIS training. In this paper, we explore the usage of different holistic features for automated skill assessment using only robot kinematic data and propose a weighted feature fusion technique for improving score prediction performance. Methods: We perform our experiments on the…
Brigham Young University（杨百翰大学）；Adobe Research（Adobe 研究院）
Hand segmentation and detection in truly unconstrained RGB-based settings is important for many applications. However, existing datasets are far from sufficient in terms of size and variety due to the infeasibility of manual annotation of large amounts of segmentation and detection data. As a result, current methods are limited by many underlying assumptions such as constrained environment, consistent skin color and lighting. In this work, we present Ego2Hands, a large-scale RGB-based egocentric hand segmentation/detection dataset that is semi-automatically annotated and a color-invariant comp…
Label-supervised surgical instrument segmentation using temporal equivariance and semantic continuity
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生物医学工程学院，生命科学与医学系）；Center for Medical Imaging, Robotics, Analytic Computing & Learning(MIRACLE), Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州先进研究院医学影像中心、机器人与分析计算与学习中心）；Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China（中国科学技术大学精密与智能化学重点实验室）；Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS), Institute of Computing Technology, CAS（中国科学院智能信息处理重点实验室，计算技术研究所）；Faculty of Hepato-Biliary-Pancreatic Surgery, The First Medical Center, Chinese PLA General Hospital（中国人民解放军总医院肝胆胰外科系）
Weakly Supervised Temporal Convolutional Networks for Fine-grained Surgical Activity Recognition
University of Verona（维罗纳大学）；University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）；IHU Strasbourg（斯特拉斯堡大学医院研究所）；University Hospital of Strasbourg（斯特拉斯堡大学医院）；IRCAD（斯特拉斯堡微创外科研究中心）；Fondazione Policlinico Universitario Agostino Gemelli IRCCS（阿戈斯蒂诺·杰梅利大学医院基金会IRCCS）
