Predicting future frames for robotic surgical video is an interesting, important yet extremely challenging problem, given that the operative tasks may have complex dynamics. Existing approaches on future prediction of natural videos were based on either deterministic models or stochastic models, including deep recurrent neural networks, optical flow, and latent space modeling. However, the potential in predicting meaningful movements of robots with dual arms in surgical scenarios has not been tapped so far, which is typically more challenging than forecasting independent motions of one arm robots in natural scenarios. In this paper, we propose a ternary prior guided variational autoencoder (TPG-VAE) model for future frame prediction in robotic surgical video sequences. Besides content distribution, our model learns motion distribution, which is novel to handle the small movements of surgical tools. Furthermore, we add the invariant prior information from the gesture class into the generation process to constrain the latent space of our model. To our best knowledge, this is the first time that the future frames of dual arm robots are predicted considering their unique characteristics relative to general robotic videos. Experiments demonstrate that our model gains more stable and realistic future frame prediction scenes with the suturing task on the public JIGSAWS dataset.
Massachusetts Institute of Technology（麻省理工学院）；Neuromation OU；University of Michigan（密歇根大学）；Lyft Inc.（来福公司）
Semantic segmentation of robotic instruments is an important problem for the robot-assisted surgery. One of the main challenges is to correctly detect an instrument's position for the tracking and pose estimation in the vicinity of surgical scenes. Accurate pixel-wise instrument segmentation is needed to address this challenge. In this paper we describe our winning solution for MICCAI 2017 Endoscopic Vision SubChallenge: Robotic Instrument Segmentation. Our approach demonstrates an improvement over the state-of-the-art results using several novel deep neural network architectures. It addressed…
NUS Graduate School for Integrative Sciences and Engineering (NGS), National University of Singapore（新加坡国立大学NUS综合科学与工程研究生院（NGS））；National University of Singapore（新加坡国立大学）；University of Michigan-Joint Institute, Shanghai Jiao Tong University（上海交通大学密歇根联合学院）
Directing of the task-specific attention while tracking instrument in surgery holds great potential in robot-assisted intervention. For this purpose, we propose an end-to-end trainable multitask learning (MTL) model for real-time surgical instrument segmentation and attention prediction. Our model is designed with a weight-shared encoder and two task-oriented decoders and optimized for the joint tasks. We introduce batch-Wasserstein (bW) loss and construct a soft attention module to refine the distinctive visual region for efficient saliency learning. For multitask optimization, it is always c…
Cochin University of Science & Technology（科钦科学技术大学）
Pedicle screw insertion technique has made revolution in the surgical treatment of spinal fractures and spinal disorders. Although X- ray fluoroscopy based navigation is popular, there is risk of prolonged exposure to X- ray radiation. Systems that have lower radiation risk are generally quite expensive. The position and orientation of the drill is clinically very important in pedicle screw fixation. In this paper, the position and orientation of the marker on the drill is determined using pattern recognition based methods, using geometric features, obtained from the input video sequence taken…
Reconstruction of the soft tissues in robotic surgery from endoscopic stereo videos is important for many applications such as intra-operative navigation and image-guided robotic surgery automation. Previous works on this task mainly rely on SLAM-based approaches, which struggle to handle complex surgical scenes. Inspired by recent progress in neural rendering, we present a novel framework for deformable tissue reconstruction from binocular captures in robotic surgery under the single-viewpoint setting. Our framework adopts dynamic neural radiance fields to represent deformable surgical scenes…
Automatic recognition of surgical activities in the operating room (OR) is a key technology for creating next generation intelligent surgical devices and workflow monitoring/support systems. Such systems can potentially enhance efficiency in the OR, resulting in lower costs and improved care delivery to the patients. In this paper, we investigate automatic surgical activity recognition in robot-assisted operations. We collect the first large-scale dataset including 400 full-length multi-perspective videos from a variety of robotic surgery cases captured using Time-of-Flight cameras. We densely…
Technische Universität München（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Intuitive Surgical Inc.（直观医疗公司）
Detection, tracking, and pose estimation of surgical instruments are crucial tasks for computer assistance during minimally invasive robotic surgery. In the majority of cases, the first step is the automatic segmentation of surgical tools. Prior work has focused on binary segmentation, where the objective is to label every pixel in an image as tool or background. We improve upon previous work in two major ways. First, we leverage recent techniques such as deep residual learning and dilated convolutions to advance binary-segmentation performance. Second, we extend the approach to multi-class se…
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）
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）
Georgia Institute of Technology（佐治亚理工学院）；Twentieth Century Fox（二十世纪福克斯公司）；Starfleet Academy（星际舰队学院）；Tyrell Inc.（泰瑞尔公司）
Video understanding of robot-assisted surgery (RAS) videos is an active research area. Modeling the gestures and skill level of surgeons presents an interesting problem. The insights drawn may be applied in effective skill acquisition, objective skill assessment, real-time feedback, and human-robot collaborative surgeries. We propose a solution to the tool detection and localization open problem in RAS video understanding, using a strictly computer vision approach and the recent advances of deep learning. We propose an architecture using multimodal convolutional neural networks for fast detect…
The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University College London（伦敦大学学院）
Surgical instrument segmentation -- in general a pixel classification task -- is fundamentally crucial for promoting cognitive intelligence in robot-assisted surgery (RAS). However, previous methods are struggling with discriminating instrument types and instances. To address the above issues, we explore a mask classification paradigm that produces per-segment predictions. We propose TraSeTR, a novel Track-to-Segment Transformer that wisely exploits tracking cues to assist surgical instrument segmentation. TraSeTR jointly reasons about the instrument type, location, and identity with instance-…
Imperial College London（伦敦帝国理工学院）；Tsinghua University（清华大学）；NYU Abu Dhabi（纽约大学阿布扎比分校）；Wuhan University（武汉大学）；University College London（伦敦大学学院）；University of Liverpool（利物浦大学）
Imperial College London（帝国理工学院）；University of Cambridge（剑桥大学）；PAII Inc（PAII公司）
In Robot-Assisted Minimally Invasive Surgery (RAMIS), a camera assistant is normally required to control the position and zooming ratio of the laparoscope, following the surgeon's instructions. However, moving the laparoscope frequently may lead to unstable and suboptimal views, while the adjustment of zooming ratio may interrupt the workflow of the surgical operation. To this end, we propose a multi-scale Generative Adversarial Network (GAN)-based video super-resolution method to construct a framework for automatic zooming ratio adjustment. It can provide automatic real-time zooming for high-…
Purpose: This paper focuses on an automated analysis of surgical motion profiles for objective skill assessment and task recognition in robot-assisted surgery. Existing techniques heavily rely on conventional statistic measures or shallow modelings based on hand-engineered features and gesture segmentation. Such developments require significant expert knowledge, are prone to errors, and are less efficient in online adaptive training systems. Methods: In this work, we present an efficient analytic framework with a parallel deep learning architecture, SATR-DL, to assess trainee expertise and rec…
Asan Medical Center（峨山医疗中心）；Johns Hopkins University（约翰斯·霍普金斯大学）；Chungnam National University School of Medicine（忠南大学医学院）
Some endoscopic surgeries require a surgeon to hold the endoscope with one hand and the surgical instruments with the other hand to perform the actual surgery with correct vision. Recent technical advances in deep learning as well as in robotics can introduce robotics to these endoscopic surgeries. This can have numerous advantages by freeing one hand of the surgeon, which will allow the surgeon to use both hands and to use more intricate and sophisticated techniques. Recently, deep learning with convolutional neural network achieves state-of-the-art results in computer vision. Therefore, the …
Autonomy is the frontier of research in robotic surgery and its aim is to improve the quality of surgical procedures in the next future. One fundamental requirement for autonomy is advanced perception capability through vision sensors. In this paper, we propose a novel calibration technique for a surgical scenario with da Vinci robot. Calibration of the camera and the robot is necessary for precise positioning of the tools in order to emulate the high performance surgeons. Our calibration technique is tailored for RGB-D camera. Different tests performed on relevant use cases for surgery prove …
Computer-assisted surgery has been developed to enhance surgery correctness and safety. However, researchers and engineers suffer from limited annotated data to develop and train better algorithms. Consequently, the development of fundamental algorithms such as Simultaneous Localization and Mapping (SLAM) is limited. This article elaborates on the efforts of preparing the dataset for semantic segmentation, which is the foundation of many computer-assisted surgery mechanisms. Based on the Cholec80 dataset [3], we extracted 8,080 laparoscopic cholecystectomy image frames from 17 video clips in C…
MIT（麻省理工学院）；Massachusetts General Hospital（麻省总医院）；Olympus Corporation（奥林巴斯公司）
Comprehension of surgical workflow is the foundation upon which artificial intelligence (AI) and machine learning (ML) holds the potential to assist intraoperative decision-making and risk mitigation. In this work, we move beyond mere identification of past surgical phases, into the prediction of future surgical steps and specification of the transitions between them. We use a novel Generative Adversarial Network (GAN) formulation to sample future surgical phases trajectories conditioned on past video frames from laparoscopic cholecystectomy (LC) videos and compare it to state-of-the-art appro…
National University of Singapore（新加坡国立大学）；Imperial College London（伦敦帝国学院）；Singapore General Hospital（新加坡中央医院）
Learning to infer graph representations and performing spatial reasoning in a complex surgical environment can play a vital role in surgical scene understanding in robotic surgery. For this purpose, we develop an approach to generate the scene graph and predict surgical interactions between instruments and surgical region of interest (ROI) during robot-assisted surgery. We design an attention link function and integrate with a graph parsing network to recognize the surgical interactions. To embed each node with corresponding neighbouring node features, we further incorporate SageConv into the …
In robotic surgery, tool tracking is important for providing safe tool-tissue interaction and facilitating surgical skills assessment. Despite recent advances in tool tracking, existing approaches are faced with major difficulties in real-time tracking of articulated tools. Most algorithms are tailored for offline processing with pre-recorded videos. In this paper, we propose a real-time 3D tracking method for articulated tools in robotic surgery. The proposed method is based on the CAD model of the tools as well as robot kinematics to generate online part-based templates for efficient 2D matc…
DARES: Depth Anything in Robotic Endoscopic Surgery with Self-supervised Vector-LoRA of the Foundation Model
Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS)（惠康/英国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）；Politecnico di Milano（米兰理工大学）；Centre for Medical Image Computing（医学影像计算中心）
Despite significant developments in the design of surgical robots and automated techniques for objective evaluation of surgical skills, there are still challenges in ensuring safety in robot-assisted minimally-invasive surgery (RMIS). This paper presents a runtime monitoring system for the detection of executional errors during surgical tasks through the analysis of kinematic data. The proposed system incorporates dual Siamese neural networks and knowledge of surgical context, including surgical tasks and gestures, their distributional similarities, and common error modes, to learn the differe…
The University of Texas at Dallas（得克萨斯大学达拉斯分校）；UT Southwestern Medical Center（得克萨斯大学西南医学中心）
With the advent of robot-assisted surgery, the role of data-driven approaches to integrate statistics and machine learning is growing rapidly with prominent interests in objective surgical skill assessment. However, most existing work requires translating robot motion kinematics into intermediate features or gesture segments that are expensive to extract, lack efficiency, and require significant domain-specific knowledge. We propose an analytical deep learning framework for skill assessment in surgical training. A deep convolutional neural network is implemented to map multivariate time series…
Fiducial markers are commonly used in navigation assisted minimally invasive spine surgery (MISS) and they help transfer image coordinates into real world coordinates. In practice, these markers might be located outside the field-of-view (FOV), due to the limited detector sizes of C-arm cone-beam computed tomography (CBCT) systems used in intraoperative surgeries. As a consequence, reconstructed markers in CBCT volumes suffer from artifacts and have distorted shapes, which sets an obstacle for navigation. In this work, we propose two fiducial marker detection methods: direct detection from dis…
SLAM assisted 3D tracking system for laparoscopic surgery
United Imaging Research Institute of Intelligent Imaging（联影智能影像研究院）；University of Michigan（密歇根大学）；United Imaging（联影医疗）；United Imaging of Surgery（联影外科研究院）
Surgical scene understanding and multi-tasking learning are crucial for image-guided robotic surgery. Training a real-time robotic system for the detection and segmentation of high-resolution images provides a challenging problem with the limited computational resource. The perception drawn can be applied in effective real-time feedback, surgical skill assessment, and human-robot collaborative surgeries to enhance surgical outcomes. For this purpose, we develop a novel end-to-end trainable real-time Multi-Task Learning (MTL) model with weight-shared encoder and task-aware detection and segment…
Technische Universität München（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Segmentation of surgical instruments is an important problem in robot-assisted surgery: it is a crucial step towards full instrument pose estimation and is directly used for masking of augmented reality overlays during surgical procedures. Most applications rely on accurate real-time segmentation of high-resolution surgical images. While previous research focused primarily on methods that deliver high accuracy segmentation masks, majority of them can not be used for real-time applications due to their computational cost. In this work, we design a light-weight and highly-efficient deep residual…
The Hamlyn Centre for Robotic Surgery, IGHI, Imperial College London（伦敦帝国理工学院哈姆林机器人手术中心）；Dyson Robotics Laboratory, Imperial College London（伦敦帝国理工学院戴森机器人实验室）；OpenAI；Department of Surgery and Cancer, Imperial College London（伦敦帝国理工学院外科与癌症系）
Robotic surgery has become a powerful tool for performing minimally invasive procedures, providing advantages in dexterity, precision, and 3D vision, over traditional surgery. One popular robotic system is the da Vinci surgical platform, which allows preoperative information to be incorporated into live procedures using Augmented Reality (AR). Scene depth estimation is a prerequisite for AR, as accurate registration requires 3D correspondences between preoperative and intraoperative organ models. In the past decade, there has been much progress on depth estimation for surgical scenes, such as …
Uncertainty-aware Self-supervised Learning for Cross-domain Technical Skill Assessment in Robot-assisted Surgery
The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Scuola Superiore Sant’Anna（圣安娜高等研究院）；Politecnico di Milano（米兰理工大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；UT Southwestern Medical Center（德克萨斯大学西南医学中心）
Robotic-assisted surgeries benefit both surgeons and patients, however, surgeons frequently need to adjust the endoscopic camera to achieve good viewpoints. Simultaneously controlling the camera and the surgical instruments is impossible, and consequentially, these camera adjustments repeatedly interrupt the surgery. Autonomous camera control could help overcome this challenge, but most existing systems are reactive, e.g., by having the camera follow the surgical instruments. We propose a predictive approach for anticipating when camera movements will occur using artificial neural networks. We…
Dynamic Robot-Assisted Surgery with Hierarchical Class-Incremental Semantic Segmentation
Department of Computer Science, University of Freiburg（弗赖堡大学计算机科学系）；Zuse School ELIZA（Zuse学校ELIZA）；NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal（神经Poly实验室，生物医学工程学院，蒙特利尔理工学院）；Mila - Québec AI Institute（魁北克人工智能研究所）；Collaborative Research Institute Intelligent Oncology (CRIION)（智能肿瘤学协作研究机构）
Image-to-Force Estimation for Soft Tissue Interaction in Robotic-Assisted Surgery Using Structured Light
School of Electronic Information Engineering, Tongji University（同济大学电子与信息工程学院）；MicroPort MedBot (Group) Company Ltd.（微创医疗机器人（集团）股份有限公司）；College of Engineering, Peking University（北京大学工学院）；City University of Hong Kong（香港城市大学）
Intuitive Surgical Inc.（直觉外科公司）；California Institute of Technology（加州理工学院）
This paper presents a technique to concurrently and jointly predict the future trajectories of surgical instruments and the future state(s) of surgical subtasks in robot-assisted surgeries (RAS) using multiple input sources. Such predictions are a necessary first step towards shared control and supervised autonomy of surgical subtasks. Minute-long surgical subtasks, such as suturing or ultrasound scanning, often have distinguishable tool kinematics and visual features, and can be described as a series of fine-grained states with transition schematics. We propose daVinciNet - an end-to-end dual…
The use of robots in minimally invasive surgery has improved the quality of standard surgical procedures. So far, only the automation of simple surgical actions has been investigated by researchers, while the execution of structured tasks requiring reasoning on the environment and the choice among multiple actions is still managed by human surgeons. In this paper, we propose a framework to implement surgical task automation. The framework consists of a task-level reasoning module based on answer set programming, a low-level motion planning module based on dynamic movement primitives, and a sit…
NUS Graduate School for Integrative Sciences and Engineering (NGS)（新加坡国立大学综合科学与工程研究生院（NGS））；National University of Singapore（新加坡国立大学）；University of Moratuwa（莫拉图瓦大学）；NIT Trichy（印度国立理工学院蒂鲁吉拉伯利分校）
Robot-assisted surgery is an emerging technology which has undergone rapid growth with the development of robotics and imaging systems. Innovations in vision, haptics and accurate movements of robot arms have enabled surgeons to perform precise minimally invasive surgeries. Real-time semantic segmentation of the robotic instruments and tissues is a crucial step in robot-assisted surgery. Accurate and efficient segmentation of the surgical scene not only aids in the identification and tracking of instruments but also provided contextual information about the different tissues and instruments be…
Suture needle localization is necessary for autonomous suturing. Previous approaches in autonomous suturing often relied on fiducial markers rather than markerless detection schemes for localizing a suture needle due to the inconsistency of markerless detections. However, fiducial markers are not practical for real-world applications and can often be occluded from environmental factors in surgery (e.g., blood). Therefore in this work, we present a robust tracking approach for estimating the 6D pose of a suture needle when using inconsistent detections. We define observation models based on sut…
OSSAR: Towards Open-Set Surgical Activity Recognition in Robot-assisted Surgery
The Chinese University of Hong Kong (CUHK)（香港中文大学）；Beijing Institute of Technology（北京理工大学）；Qilu Hospital of Shandong University（山东大学齐鲁医院）；Tongji University（同济大学）；University College London（伦敦大学学院）
Our previous work classified a taxonomy of suturing gestures during a vesicourethral anastomosis of robotic radical prostatectomy in association with tissue tears and patient outcomes. Herein, we train deep-learning based computer vision (CV) to automate the identification and classification of suturing gestures for needle driving attempts. Using two independent raters, we manually annotated live suturing video clips to label timepoints and gestures. Identification (2395 videos) and classification (511 videos) datasets were compiled to train CV models to produce two- and five-class label predi…
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）
Objective: Surgical activity recognition is a fundamental step in computer-assisted interventions. This paper reviews the state-of-the-art in methods for automatic recognition of fine-grained gestures in robotic surgery focusing on recent data-driven approaches and outlines the open questions and future research directions. Methods: An article search was performed on 5 bibliographic databases with the following search terms: robotic, robot-assisted, JIGSAWS, surgery, surgical, gesture, fine-grained, surgeme, action, trajectory, segmentation, recognition, parsing. Selected articles were classif…
Conventional computer-assisted orthopaedic navigation systems rely on the tracking of dedicated optical markers for patient poses, which makes the surgical workflow more invasive, tedious, and expensive. Visual tracking has recently been proposed to measure the target anatomy in a markerless and effortless way, but the existing methods fail under real-world occlusion caused by intraoperative interventions. Furthermore, such methods are hardware-specific and not accurate enough for surgical applications. In this paper, we propose a RGB-D sensing-based markerless tracking method that is robust a…
Computer vision and robotics are being increasingly applied in medical interventions. Especially in interventions where extreme precision is required they could make a difference. One such application is robot-assisted retinal microsurgery. In recent works, such interventions are conducted under a stereo-microscope, and with a robot-controlled surgical tool. The complementarity of computer vision and robotics has however not yet been fully exploited. In order to improve the robot control we are interested in 3D reconstruction of the anatomy and in automatic tool localization using a stereo mic…
Technische Universität München（慕尼黑工业大学）；Carl Zeiss Meditec（卡尔蔡司医疗技术公司）；Sun Yat-Sen University（中山大学）；Klinikum rechts der Isar der Technische Universität München（慕尼黑工业大学 Rechts der Isar 医院）
Ophthalmic microsurgery is known to be a challenging operation, which requires very precise and dexterous manipulation. Image guided robot-assisted surgery (RAS) is a promising solution that brings significant improvements in outcomes and reduces the physical limitations of human surgeons. However, this technology must be further developed before it can be routinely used in clinics. One of the problems is the lack of proper calibration between the robotic manipulator and appropriate imaging device. In this work, we developed a flexible framework for hand-eye calibration of an ophthalmic robot …
Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London（伦敦大学学院威康/EPSRC介入与外科科学中心）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）
Semantic tool segmentation in surgical videos is important for surgical scene understanding and computer-assisted interventions as well as for the development of robotic automation. The problem is challenging because different illumination conditions, bleeding, smoke and occlusions can reduce algorithm robustness. At present labelled data for training deep learning models is still lacking for semantic surgical instrument segmentation and in this paper we show that it may be possible to use robot kinematic data coupled with laparoscopic images to alleviate the labelling problem. We propose a ne…
Can DeepSeek Reason Like a Surgeon? An Empirical Evaluation for Vision-Language Understanding in Robotic-Assisted Surgery
The Chinese University of Hong Kong（香港中文大学）；University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）；ICube（ICube实验室）；IHU Strasbourg（斯特拉斯堡大学医院研究所）
SEDMamba: Enhancing Selective State Space Modelling with Bottleneck Mechanism and Fine-to-Coarse Temporal Fusion for Efficient Error Detection in Robot-Assisted Surgery
UCL Hawkes Institute（伦敦大学学院霍克斯研究所）；University College London（伦敦大学学院）；Griffin Institute（格里芬研究所）；Northwick Park and St Mark’s Hospital（诺斯威克公园与圣马克医院）
The Chinese University of Hong Kong（香港中文大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Automatic surgical gesture recognition is fundamentally important to enable intelligent cognitive assistance in robotic surgery. With recent advancement in robot-assisted minimally invasive surgery, rich information including surgical videos and robotic kinematics can be recorded, which provide complementary knowledge for understanding surgical gestures. However, existing methods either solely adopt uni-modal data or directly concatenate multi-modal representations, which can not sufficiently exploit the informative correlations inherent in visual and kinematics data to boost gesture recogniti…
NUS Graduate School for Integrative Sciences and Engineering (NGS), National University of Singapore（新加坡国立大学综合科学与工程研究生院）；Dept. of Biomedical Engineering, National University of Singapore（新加坡国立大学生物医学工程系）；Dept. of Instrumentation and Control Engineering, NIT Trichy（印度国立理工学院蒂鲁奇拉帕利分校仪器与控制工程系）；Dept. of Otolaryngology, Singapore General Hospital（新加坡中央医院耳鼻喉科）
Representation learning of the task-oriented attention while tracking instrument holds vast potential in image-guided robotic surgery. Incorporating cognitive ability to automate the camera control enables the surgeon to concentrate more on dealing with surgical instruments. The objective is to reduce the operation time and facilitate the surgery for both surgeons and patients. We propose an end-to-end trainable Spatio-Temporal Multi-Task Learning (ST-MTL) model with a shared encoder and spatio-temporal decoders for the real-time surgical instrument segmentation and task-oriented saliency dete…
