A Fast Unsupervised Approach for Multi-Modality Surgical Trajectory Segmentation

Zhao, Hongfa; Xie, Jiexin; Shao, Zhenzhou; Qu, Ying; Guan, Yong; Tan, Jindong

doi:10.1109/access.2018.2872635

Cited by 13 publications

(16 citation statements)

References 20 publications

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“…The kinematic signals were then integrated with visual features extracted with a pre-trained CNN, improving segmentation performance [64]. TCN methodology was later extended using Dense Convolutional Encoder-Decoder Netowork (DCED-Net) for unsupervised feature extraction from surgical videos [65]. Trained on the task of image reconstruction, DCED-Net extracts discriminative visual features using specific convolutional blocks, called Dense Blocks, to reduce information loss during dimensionality reduction.…”

Section: Unsupervised Learningmentioning

confidence: 99%

“…Trained on the task of image reconstruction, DCED-Net extracts discriminative visual features using specific convolutional blocks, called Dense Blocks, to reduce information loss during dimensionality reduction. Segmentation results were further improved with a majority vote strategy to eliminate spurious transition points exploiting both kinematic and visual information, and an iterative algorithm to merge similar adjacent segments [65].…”

Section: Deep Reinforcement Learningmentioning

confidence: 99%

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Gesture Recognition in Robotic Surgery: A Review

Amsterdam

Clarkson²,

Stoyanov³

2021

IEEE Trans. Biomed. Eng.

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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 combinations of the following search terms: robotic, robot-assisted, JIGSAWS, surgery, surgical, gesture, fine-grained, surgeme, action, trajectory, segmentation, recognition, parsing. Selected articles were classified based on the level of supervision required for training and divided into different groups representing major frameworks for time series analysis and data modelling. Results: A total of 52 articles were reviewed. The research field is showing rapid expansion, with the majority of articles published in the last 4 years. Deep-learning-based temporal models with discriminative feature extraction and multi-modal data integration have demonstrated promising results on small surgical datasets. Currently, unsupervised methods perform significantly less well than the supervised approaches. Conclusion: The development of large and diverse open-source datasets of annotated demonstrations is essential for development and validation of robust solutions for surgical gesture recognition. While new strategies for discriminative feature extraction and knowledge transfer, or unsupervised and semi-supervised approaches, can mitigate the need for data and labels, they have not yet been demonstrated to achieve comparable performance. Important future research directions include detection and forecast of gesture-specific errors and anomalies. Significance: This paper is a comprehensive and structured analysis of surgical gesture recognition methods aiming to summarize the status of this rapidly evolving field.

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Section: Unsupervised Learningmentioning

confidence: 99%

Section: Deep Reinforcement Learningmentioning

confidence: 99%

Gesture Recognition in Robotic Surgery: A Review

Amsterdam

Clarkson²,

Stoyanov³

2021

IEEE Trans. Biomed. Eng.

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“…Kinematic data is also used for the task of instruments trajectory segmentation. Zhao et al [76] presented an unsupervised network for tools trajectory segmentation based on laparoscopic image and kinematic data. This work is based on a structure of dense connection, in which the first half of the network, the dense block, is an encoder that performs feature extraction, the transition layer performs the trajectory segmentation, and the up-sampling layer is used for image reconstruction.…”

Section: ) Trajectory Segmentationmentioning

confidence: 99%

A Review on Deep Learning in Minimally Invasive Surgery

et al. 2021

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In the last five years, deep learning has attracted great interest in computer-assisted systems for Minimally Invasive Surgery. The straightforward accessibility to images in surgical interventions makes deep neural networks enormously powerful for solving classification problems in complex surgical scenarios. The objective of this work is to provide readers a survey on deep learning models applied to minimally invasive surgery, identifying the different architectures used depending on the application, the results achieved until now, and the publicly available surgical datasets that can be used for validating new studies. A total of 85 publications have been extracted from manual research from four databases (IEEE Xplorer, Springer Link, Science Direct, and ACM Digital Library). After analyzing all these studies, they have been classified into four applications: surgical image analysis, surgical task analysis, surgical skill assessment, and automation of surgical tasks. This work provides a technical description of these works and a comparison among them. Finally, promising research directions to advance in this field are identified.

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“…Zhao et al proposed a unsupervised segmentation method for multi-modality surgical trajectory segmentation based on PMDD which include four types of similarity indicators like PCA (principal component analysis), MI (mutual information), DCS (distance between centre of segments) and dynamic time warping (DTW) which is used to measure similarity between two segments [12]. PCA is used to reduce the redundancy of data which determines the internal structure and similarity between the segments.…”

Section: Active Edge Movementmentioning

confidence: 99%

“…The DTW is used to compute the similarity between two set of sequences by fitting the sequences in time domain. Finally by using these four similarity measures computed the final similarity is calculated and the segments with high similarity are merged to get the final segmented image [12]. The comparison of various segmentation algorithms are shown in Table 1.…”

Section: Active Edge Movementmentioning

confidence: 99%

A Performance Comparison of Supervised and Unsupervised Image Segmentation Methods

Baby

Devaraj

Mathew³

et al. 2020

SN COMPUT. SCI.

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Image processing plays a vital role in many recent computer applications in the association with machine learning technology. The supervised training on dataset of features can only be successful if the segmentation process is accurate in the computer vision phase. The term segmentation is the process of extracting or identification of distinguishable regions in an image. This is performed based on the properties of image pixel intensity values and their proximities. This paper mainly focuses on an investigation of various latest image segmentation techniques performed in the field of computer vision and image processing. Segmentation plays a vital role in computer vision since any fault in segmentation will led to inaccurate extraction of features which results in wrong prediction of the decision support systems.

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A Fast Unsupervised Approach for Multi-Modality Surgical Trajectory Segmentation

Cited by 13 publications

References 20 publications

Gesture Recognition in Robotic Surgery: A Review

Gesture Recognition in Robotic Surgery: A Review

A Review on Deep Learning in Minimally Invasive Surgery

A Performance Comparison of Supervised and Unsupervised Image Segmentation Methods

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