Automatic surgical phase recognition is a challenging and crucial task with the potential to improve patient safety and become an integral part of intra-operative decision-support systems. In this paper, we propose, for the first time in workflow analysis, a Multi-Stage Temporal Convolutional Network (MS-TCN) that performs hierarchical prediction refinement for surgical phase recognition. Causal, dilated convolutions allow for a large receptive field and online inference with smooth predictions even during ambiguous transitions. Our method is thoroughly evaluated on two datasets of laparoscopic cholecystectomy videos with and without the use of additional surgical tool information. Outperforming various state-of-the-art LSTM approaches, we verify the suitability of the proposed causal MS-TCN for surgical phase recognition.
Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）
Surgical workflow analysis is of importance for understanding onset and persistence of surgical phases and individual tool usage across surgery and in each phase. It is beneficial for clinical quality control and to hospital administrators for understanding surgery planning. Video acquired during surgery typically can be leveraged for this task. Currently, a combination of convolutional neural network (CNN) and recurrent neural networks (RNN) are popularly used for video analysis in general, not only being restricted to surgical videos. In this paper, we propose a multi-task learning framework…
The Chinese University of Hong Kong（香港中文大学）；Imsight Medical Technology, Co, Ltd（Imsight医疗科技有限公司）；The Hong Kong Polytechnic University（香港理工大学）
Surgical tool presence detection and surgical phase recognition are two fundamental yet challenging tasks in surgical video analysis and also very essential components in various applications in modern operating rooms. While these two analysis tasks are highly correlated in clinical practice as the surgical process is well-defined, most previous methods tackled them separately, without making full use of their relatedness. In this paper, we present a novel method by developing a multi-task recurrent convolutional network with correlation loss (MTRCNet-CL) to exploit their relatedness to simult…
Bournemouth University（伯恩茅斯大学）；Communication University of China（中国传媒大学）
Automatic surgical gesture recognition is a prerequisite of intra-operative computer assistance and objective surgical skill assessment. Prior works either require additional sensors to collect kinematics data or have limitations on capturing temporal information from long and untrimmed surgical videos. To tackle these challenges, we propose a novel temporal convolutional architecture to automatically detect and segment surgical gestures with corresponding boundaries only using RGB videos. We devise our method with a symmetric dilation structure bridged by a self-attention module to encode and…
Renal cell carcinoma (RCC) is a type of cancer that originates in the kidneys and is the most common type of kidney cancer in adults. It can be classified into several subtypes, including clear cell RCC, papillary RCC, and chromophobe RCC. In this study, an artificial intelligence model was developed and trained for classifying different subtypes of RCC using ResNet-18, a convolutional neural network that has been widely used for image classification tasks. The model was trained on a dataset of RCC histopathology images, which consisted of digital images of RCC surgical resection slides that w…
Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）
Providing force feedback as relevant information in current Robot-Assisted Minimally Invasive Surgery systems constitutes a technological challenge due to the constraints imposed by the surgical environment. In this context, Sensorless Force Estimation techniques represent a potential solution, enabling to sense the interaction forces between the surgical instruments and soft-tissues. Specifically, if visual feedback is available for observing soft-tissues' deformation, this feedback can be used to estimate the forces applied to these tissues. To this end, a force estimation model, based on Co…
Seven million people suffer surgical complications each year, but with sufficient surgical training and review, 50\% of these complications could be prevented. To improve surgical performance, existing research uses various deep learning (DL) technologies including convolutional neural networks (CNN) and recurrent neural networks (RNN) to automate surgical tool and workflow detection. However, there is room to improve accuracy; real-time analysis is also minimal due to the complexity of CNN. In this research, a novel DL architecture is proposed to integrate visual simultaneous localization and…
Tecnologico de Monterrey（蒙特雷科技大学）；University of Oxford（牛津大学）；Oxford NIHR Biomedical Research Centre（牛津国家卫生研究院生物医学研究中心）
Precise instrument segmentation aid surgeons to navigate the body more easily and increase patient safety. While accurate tracking of surgical instruments in real-time plays a crucial role in minimally invasive computer-assisted surgeries, it is a challenging task to achieve, mainly due to 1) complex surgical environment, and 2) model design with both optimal accuracy and speed. Deep learning gives us the opportunity to learn complex environment from large surgery scene environments and placements of these instruments in real world scenarios. The Robust Medical Instrument Segmentation 2019 cha…
Department of Information Technology, Alpen-Adria-Universität Klagenfurt（克恩滕州阿尔卑斯-亚得里亚大学信息技术系）；Department of Ophthalmology, Klinikum Klagenfurt（克拉根福医院眼科）
Semantic segmentation in surgical videos is a prerequisite for a broad range of applications towards improving surgical outcomes and surgical video analysis. However, semantic segmentation in surgical videos involves many challenges. In particular, in cataract surgery, various features of the relevant objects such as blunt edges, color and context variation, reflection, transparency, and motion blur pose a challenge for semantic segmentation. In this paper, we propose a novel convolutional module termed as \textit{ReCal} module, which can calibrate the feature maps by employing region intra-an…
Xiamen University（厦门大学）；The Chinese University of Hong Kong（香港中文大学）；Zhongshan Hospital affiliated to Xiamen University（厦门大学附属中山医院）；The Hong Kong Polytechnic University（香港理工大学）
Performing a real-time and accurate instrument segmentation from videos is of great significance for improving the performance of robotic-assisted surgery. We identify two important clues for surgical instrument perception, including local temporal dependency from adjacent frames and global semantic correlation in long-range duration. However, most existing works perform segmentation purely using visual cues in a single frame. Optical flow is just used to model the motion between only two frames and brings heavy computational cost. We propose a novel dual-memory network (DMNet) to wisely relat…
Graduate School of Information Science, Nagoya University（名古屋大学信息科学研究生院）；Graduate School of Informatics, Nagoya University（名古屋大学信息学院研究生院）；School of Information Science, Aichi Institute of Technology（爱知工业大学信息学院）；Nagoya University Graduate School of Medicine（名古屋大学医学研究生院）
This paper presents a new approach for precisely estimating the renal vascular dominant region using a Voronoi diagram. To provide computer-assisted diagnostics for the pre-surgical simulation of partial nephrectomy surgery, we must obtain information on the renal arteries and the renal vascular dominant regions. We propose a fully automatic segmentation method that combines a neural network and tensor-based graph-cut methods to precisely extract the kidney and renal arteries. First, we use a convolutional neural network to localize the kidney regions and extract tiny renal arteries with a ten…
Graduate School of Informatics, Kyoto University（京都大学信息学研究科）；Graduate School of Medicine, Kyoto University（京都大学医学研究科）
Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a 3D organ mesh for a single-viewpoint 2D projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiati…
High-risk Factor Prediction in Lung Cancer Using Thin CT Scans: An Attention-Enhanced Graph Convolutional Network Approach
Renmin University of China（中国人民大学）；Capital Medical University（首都医科大学）；Beijing Chao-Yang Hospital（北京朝阳医院）；Beijing Institute of Respiratory Medicine（北京呼吸疾病研究所）
Technical University of Kosice（科希策工业大学）；Pavol Jozef Safarik University（帕沃尔·约瑟夫·沙法里克大学）；Louis Pasteur University Hospital（路易斯·巴斯德大学医院）
The kidney cancer is one of the most common cancer types. The treatment frequently include surgical intervention. However, surgery is in this case particularly challenging due to regional anatomical relations. Organ delineation can significantly improve surgical planning and execution. In this contribution, we propose ensemble of two fully convolutional networks for segmentation of kidney, tumor, veins and arteries. While SegResNet architecture achieved better performance on tumor, the nnU-Net provided more precise segmentation for kidneys, arteries and veins. So in our proposed approach we co…
Johns Hopkins University（约翰斯·霍普金斯大学）；University of British Columbia（不列颠哥伦比亚大学）
Due to imaging artifacts and low signal-to-noise ratio in ultrasound images, automatic bone surface segmentation networks often produce fragmented predictions that can hinder the success of ultrasound-guided computer-assisted surgical procedures. Existing pixel-wise predictions often fail to capture the accurate topology of bone tissues due to a lack of supervision to enforce connectivity. In this work, we propose an orientation-guided graph convolutional network to improve connectivity while segmenting the bone surface. We also propose an additional supervision on the orientation of the bone …
University Hospital Heidelberg（海德堡大学医院）；Otto-von-Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）
Minimally Invasive Surgery (MIS) techniques have gained rapid popularity among surgeons since they offer significant clinical benefits including reduced recovery time and diminished post-operative adverse effects. However, conventional endoscopic systems output monocular video which compromises depth perception, spatial orientation and field of view. Suturing is one of the most complex tasks performed under these circumstances. Key components of this tasks are the interplay between needle holder and the surgical needle. Reliable 3D localization of needle and instruments in real time could be u…
Shanghai United Imaging Intelligence Co. Ltd（上海联影智能医疗科技有限公司）；Southern Medical University（南方医科大学）
In brain tumor diagnosis and surgical planning, segmentation of tumor regions and accurate analysis of surrounding normal tissues are necessary for physicians. Pathological variability often renders difficulty to register a well-labeled normal atlas to such images and to automatic segment/label surrounding normal brain tissues. In this paper, we propose a new registration approach that first segments brain tumor using a U-Net and then simulates missed normal tissues within the tumor region using a partial convolutional network. Then, a standard normal brain atlas image is registered onto such …
PAII Inc.（PAII 公司）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）；Ping An Technology（平安科技）
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and carries a dismal prognosis. Surgery remains the best chance of a potential cure for patients who are eligible for initial resection of PDAC. However, outcomes vary significantly even among the resected patients of the same stage and received similar treatments. Accurate preoperative prognosis of resectable PDACs for personalized treatment is thus highly desired. Nevertheless, there are no automated methods yet to fully exploit the contrast-enhanced computed tomography (CE-CT) imaging for PDAC. Tumor attenuation chang…
School of Electrical & Computer Engineering, University of Tehran（德黑兰大学电气与计算机工程学院）；Graduate School of Information Science, Nara Institute of Science and Technology（奈良科学技术研究院信息科学研究生院）；Osaka University Graduate School of Medicine（大阪大学医学院研究生院）
Alignment of the bones in standing position provides useful information in surgical planning. In total hip arthroplasty (THA), pelvic sagittal inclination (PSI) angle in the standing position is an important factor in planning of cup alignment and has been estimated mainly from radiographs. Previous methods for PSI estimation used a patient-specific CT to create digitally reconstructed radiographs (DRRs) and compare them with the radiograph to estimate relative position between the pelvis and the x-ray detector. In this study, we developed a method that estimates PSI angle from a single antero…
Postoperative glioblastoma segmentation: Development of a fully automated pipeline using deep convolutional neural networks and comparison with currently available models
Río Hortega University Hospital（里奥·奥尔特加大学医院）；Universidad de Valladolid（巴利亚多利德大学）；Center for Biomedical Research in Network of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)（生物工程、生物材料和纳米医学网络生物医学研究中心）；Albacete University Hospital（阿尔瓦塞特大学医院）；La Princesa University Hospital（拉普林塞萨大学医院）；UiT The Arctic University of Norway（挪威北极大学）；University Hospital of North Norway（北挪威大学医院）；St. Olavs University Hospital（圣奥拉夫大学医院）；Norwegian University of Science and Technology（挪威科技大学）；St. Olavs Hospital, Trondheim University Hospital（圣奥拉夫医院，特隆赫姆大学医院）；de Octubre University Hospital (i + 12)（10月12日大学医院）；Complutense University（马德里康普顿斯大学）；Instituto de Investigación Sanitaria, 12 de Octubre University Hospital (i + 12)（10月12日大学医院卫生研究所）；Institute for Research in Mathematics (IMUVA), University of Valladolid（巴利亚多利德大学数学研究所）
RSIP Vision（RSIP Vision 公司）；Weizmann Institute of Science（魏茨曼科学研究所）
We present an end-to-end Convolutional Neural Network (CNN) approach for 3D reconstruction of knee bones directly from two bi-planar X-ray images. Clinically, capturing the 3D models of the bones is crucial for surgical planning, implant fitting, and postoperative evaluation. X-ray imaging significantly reduces the exposure of patients to ionizing radiation compared to Computer Tomography (CT) imaging, and is much more common and inexpensive compared to Magnetic Resonance Imaging (MRI) scanners. However, retrieving 3D models from such 2D scans is extremely challenging. In contrast to the commo…
Imperial College London（伦敦帝国理工学院）；University of Oxford（牛津大学）；Lightpoint Medical Ltd.（光点医疗有限公司）
Dense depth estimation and 3D reconstruction of a surgical scene are crucial steps in computer assisted surgery. Recent work has shown that depth estimation from a stereo images pair could be solved with convolutional neural networks. However, most recent depth estimation models were trained on datasets with per-pixel ground truth. Such data is especially rare for laparoscopic imaging, making it hard to apply supervised depth estimation to real surgical applications. To overcome this limitation, we propose SADepth, a new self-supervised depth estimation method based on Generative Adversarial N…
Medical University of Warsaw（华沙医科大学）；University of Warsaw（华沙大学）；SGH Warsaw School of Economics（SGH华沙经济学院）；Carolina Medical Center（卡罗莱纳医疗中心）；Warsaw University of Technology（华沙理工大学）；Tooploox
Achilles tendon rupture is a debilitating injury, which is typically treated with surgical repair and long-term rehabilitation. The recovery, however, is protracted and often incomplete. Diagnosis, as well as healing progress assessment, are largely based on ultrasound and magnetic resonance imaging. In this paper, we propose an automatic method based on deep learning for analysis of Achilles tendon condition and estimation of its healing progress on ultrasound images. We develop custom convolutional neural networks for classification and regression on healing score and feature extraction. Our…
Chongqing University of Posts and Telecommunications（重庆邮电大学）；Stomatological Hospital of Chongqing Medical University（重庆医科大学附属口腔医院）；Macau University of Science and Technology（澳门科技大学）；Xi’an Jiaotong University（西安交通大学）；ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能科技有限公司）；The University of Hong Kong（香港大学）
Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i.e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation. However, since different raw attributes reveal completely different geometric information, the naive concatenation of different raw attributes at the (low-level) input stage may bring unnecessary confusion in describing and dif…
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；Osaka University（大阪大学）
In total hip arthroplasty, analysis of postoperative medical images is important to evaluate surgical outcome. Since Computed Tomography (CT) is most prevalent modality in orthopedic surgery, we aimed at the analysis of CT image. In this work, we focus on the metal artifact in postoperative CT caused by the metallic implant, which reduces the accuracy of segmentation especially in the vicinity of the implant. Our goal was to develop an automated segmentation method of the bones and muscles in the postoperative CT images. We propose a method that combines Normalized Metal Artifact Reduction (NM…
With the injection of contrast material into blood vessels, multi-phase contrasted CT images can enhance the visibility of vessel networks in the human body. Reconstructing the 3D geometric morphology of liver vessels from the contrasted CT images can enable multiple liver preoperative surgical planning applications. Automatic reconstruction of liver vessel morphology remains a challenging problem due to the morphological complexity of liver vessels and the inconsistent vessel intensities among different multi-phase contrasted CT images. On the other side, high integrity is required for the 3D…
SimulaMet（西穆拉梅特研究所）；UIT The Arctic University of Norway（挪威北极大学）；University of Oxford（牛津大学）；Oslo Metropolitan University（奥斯陆城市大学）
Colonoscopy is the gold standard for examination and detection of colorectal polyps. Localization and delineation of polyps can play a vital role in treatment (e.g., surgical planning) and prognostic decision making. Polyp segmentation can provide detailed boundary information for clinical analysis. Convolutional neural networks have improved the performance in colonoscopy. However, polyps usually possess various challenges, such as intra-and inter-class variation and noise. While manual labeling for polyp assessment requires time from experts and is prone to human error (e.g., missed lesions)…
