Intra-operative anticipation of instrument usage is a necessary component for context-aware assistance in surgery, e.g. for instrument preparation or semi-automation of robotic tasks. However, the sparsity of instrument occurrences in long videos poses a challenge. Current approaches are limited as they assume knowledge on the timing of future actions or require dense temporal segmentations during training and inference. We propose a novel learning task for anticipation of instrument usage in laparoscopic videos that overcomes these limitations. During training, only sparse instrument annotations are required and inference is done solely on image data. We train a probabilistic model to address the uncertainty associated with future events. Our approach outperforms several baselines and is competitive to a variant using richer annotations. We demonstrate the model's ability to quantify task-relevant uncertainties. To the best of our knowledge, we are the first to propose a method for anticipating instruments in surgery.
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…
Surgical workflow anticipation can give predictions on what steps to conduct or what instruments to use next, which is an essential part of the computer-assisted intervention system for surgery, e.g. workflow reasoning in robotic surgery. However, current approaches are limited to their insufficient expressive power for relationships between instruments. Hence, we propose a graph representation learning framework to comprehensively represent instrument motions in the surgical workflow anticipation problem. In our proposed graph representation, we maps the bounding box information of instrument…
Simon Fraser University（西蒙菲莎大学）；Borealis AI（北极光人工智能研究院）
Action anticipation involves predicting future actions having observed the initial portion of a video. Typically, the observed video is processed as a whole to obtain a video-level representation of the ongoing activity in the video, which is then used for future prediction. We introduce ANTICIPATR which performs long-term action anticipation leveraging segment-level representations learned using individual segments from different activities, in addition to a video-level representation. We propose a two-stage learning approach to train a novel transformer-based model that uses these two types …
hutom；Yonsei University College of Medicine（延世大学医学院）
As the area of application of deep neural networks expands to areas requiring expertise, e.g., in medicine and law, more exquisite annotation processes for expert knowledge training are required. In particular, it is difficult to guarantee generalization performance in the clinical field in the case of expert knowledge training where opinions may differ even among experts on annotations. To raise the issue of the annotation generation process for expertise training of CNNs, we verified the annotations for surgical phase recognition of laparoscopic cholecystectomy and subtotal gastrectomy for g…
SurgAnt-ViVQA: Learning to Anticipate Surgical Events through GRU-Driven Temporal Cross-Attention
UCL Hawkes Institute（UCL哈维斯研究所）；University College London（伦敦大学学院）；Dept of Medical Physics & Biomedical Engineering（医学物理与生物医学工程系）；UCL（伦敦大学学院）；Dept of Computer Science（计算机科学系）；National Hospital for Neurology and Neurosurgery（神经病学与神经外科国家医院）；Division of Informatics, Imaging and Data Science（信息学、成像与数据科学 division）
The Chinese University of Hong Kong（香港中文大学）；Imperial College London（帝国理工学院）；National University of Singapore（新加坡国立大学）；Shun Hing Institute of Advanced Engineering（信兴高等工程研究所）
Data diversity and volume are crucial to the success of training deep learning models, while in the medical imaging field, the difficulty and cost of data collection and annotation are especially huge. Specifically in robotic surgery, data scarcity and imbalance have heavily affected the model accuracy and limited the design and deployment of deep learning-based surgical applications such as surgical instrument segmentation. Considering this, we rethink the surgical instrument segmentation task and propose a one-to-many data generation solution that gets rid of the complicated and expensive pr…
The Chinese University of Hong Kong（香港中文大学）；National University of Singapore（新加坡国立大学）；Imperial College London（帝国理工学院）；National University of Singapore (Suzhou) Research Institute（新加坡国立大学（苏州）研究院）
Surgical captioning plays an important role in surgical instruction prediction and report generation. However, the majority of captioning models still rely on the heavy computational object detector or feature extractor to extract regional features. In addition, the detection model requires additional bounding box annotation which is costly and needs skilled annotators. These lead to inference delay and limit the captioning model to deploy in real-time robotic surgery. For this purpose, we design an end-to-end detector and feature extractor-free captioning model by utilizing the patch-based sh…
Purpose: Vision-based robot tool segmentation plays a fundamental role in surgical robots and downstream tasks. CaRTS, based on a complementary causal model, has shown promising performance in unseen counterfactual surgical environments in the presence of smoke, blood, etc. However, CaRTS requires over 30 iterations of optimization to converge for a single image due to limited observability. Method: To address the above limitations, we take temporal relation into consideration and propose a temporal causal model for robot tool segmentation on video sequences. We design an architecture named Te…
On the Pitfalls of Batch Normalization for End-to-End Video Learning: A Study on Surgical Workflow Analysis
National Center for Tumor Diseases (NCT/UCC)（国家肿瘤疾病中心（NCT/UCC））；German Cancer Research Center (DKFZ)（德国癌症研究中心）；Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯医学院及大学医院）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（德累斯顿-罗森多夫亥姆霍兹中心）；Centre for Tactile Internet with Human-in-the-Loop (CeTI), TUD Dresden University of Technology（德累斯顿工业大学人在回路触觉互联网中心）
