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检索条件"主题词=Surgical gesture segmentation"

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Deep Reinforcement Learning for surgical gesture segmentation and Classification 21st

Deep Reinforcement Learning for Surgical Gesture Segmentatio...

引用

21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

作者： Liu, Daochang Jiang, Tingting Peking Univ Sch EECS Cooperat Medianet Innovat Ctr Natl Engn Lab Video Technol Beijing 100871 Peoples R China

ISBN: (纸本)9783030009373;9783030009366

Recognition of surgical gesture is crucial for surgical skill assessment and efficient surgery training. Prior works on this task are based on either variant graphical models such as HMMs and CRFs, or deep learning models such as Recurrent Neural Networks and Temporal Convolutional Networks. Most of the current approaches usually suffer from over-segmentation and therefore low segment-level edit scores. In contrast, we present an essentially different methodology by modeling the task as a sequential decision-making process. An intelligent agent is trained using reinforcement learning with hierarchical features from a deep model. Temporal consistency is integrated into our action design and reward mechanism to reduce over-segmentation errors. Experiments on JIGSAWS dataset demonstrate that the proposed method performs better than state-of-the-art methods in terms of the edit score and on par in frame-wise accuracy. Our code will be released later.

关键词： surgical gesture segmentation surgical gesture classification Deep reinforcement learning Time series analysis

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surgical gesture segmentation and Recognition

Surgical Gesture Segmentation and Recognition

引用

16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

作者： Tao, Lingling Zappella, Luca Hager, Gregory D. Vidal, Rene Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9783642407604;9783642407598

Automatic surgical gesture segmentation and recognition can provide useful feedback for surgical training in robotic surgery. Most prior work in this field relies on the robot's kinematic data. Although recent work [1,2] shows that the robot's video data can be equally effective for surgical gesture recognition, the segmentation of the video into gestures is assumed to be known. In this paper, we propose a framework for joint segmentation and recognition of surgical gestures from kinematic and video data. Unlike prior work that relies on either frame-level kinematic cues, or segment-level kinematic or video cues, our approach exploits both cues by using a combined Markov/semi-Markov conditional random field (MsM-CRF) model. Our experiments show that the proposed model improves over a Markov or semi-Markov CRF when using video data alone, gives results that are comparable to state-of-the-art methods on kinematic data alone, and improves over state-of-the-art methods when combining kinematic and video data.

关键词： surgical gesture segmentation surgical gesture recognition time series analysis conditional random fields structured output learning

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Deep Reinforcement Learning in Healthcare and Bio-Medical Applications 5

Deep Reinforcement Learning in Healthcare and Bio-Medical Ap...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Thakur, Payal Talwandi, Navjot Singh Chandigarh University Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350383522

Deep reinforcement learning (DRL), which learns a set of behaviors that maximize the projected reward, combines the representational power of deep neural networks with the reinforcement learning paradigm. DRL holds great promise for the future of healthcare and medicine, according to recent *** overview of the research on DRL in medical imaging is provided in this article. We start with a comprehensive DRL course that covers both the most recent model-based and model-free approaches. The tasks covered in the next section of this article are loosely divided into three main categories: (i) parametric medical image analysis tasks like landmark detection,object/lesion detection, registration, and view plane localization;optimization tasks like hyperparameter tuning, augmentation strategy selection, and neural architecture search;and (iii) other applications like surgical gesture segmentation, person tracking, and perso The study finishes with thoughts of potential future directions. © 2024 IEEE.

关键词： Deep reinforcement learning (DRL) Healthcare Hyper-parameter Tuning surgical gesture segmentation

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surgical gesture recognition with time delay neural network based on kinematic data

Surgical gesture recognition with time delay neural network ...

引用

International Symposium on Medical Robotics (ISMR)

作者： Menegozzo, Giovanni Dall'Alba, Diego Zandona, Chiara Fiorini, Paolo Univ Verona Dept Comp Sci Verona Italy

ISBN: (纸本)9781538678251

Automatic gesture recognition during surgical procedures is an enabling technology for improving advanced assistance features in surgical robotic systems (SRSs). Examples of such advanced features are user-specific feedback during execution of complex actions, prompt detection of safety-critical situations and autonomous execution of procedure sub-steps. Video data are available for all minimally invasive surgical procedures, but SRS could also provide accurate movements measurements based on kinematic data. Kinematic data provide low dimensional features for gesture recognition that would enable on-line processing during data acquisition. Therefore, we propose a Time Delay Neural Network (TDNN) applied to kinematic data for introducing temporal modelling in gesture recognition. We evaluate accuracy and precision of the proposed method on public benchmark dataset for surgical gesture recognition (JIGSAWS). To evaluate the generalization capability of the proposed method, we acquired a new dataset introducing a different training exercise executed in virtual environment. The dataset is publicly available to enable other methods to be tested on it. The obtained results are comparable with other methods available in literature keeping also computational performance compatible with on-line processing during surgical procedure. The proposed method and the novel dataset are key-components in the development of future autonomous SRSs with advanced situation awareness capabilities.

关键词： Time Delay Neural Network TDNN surgical gesture segmentation surgical action/ gesture recognition kinematic modelling

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