MASSD: Multi-scale attention single shot detector for surgical instruments

Yu, Lei; Wang, Pengcheng; Yan, Yusheng; Xia, Yongqiang; Cao, Wei

doi:10.1016/j.compbiomed.2020.103867

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…The CNN is the most applied DL method followed by Long Short Term Memory (LSTM), Recurrent Neural Network (RNN) and autoencoder architectures (Figure 5). The CNN model and the variants, with few modifications in the underlying architecture in some cases, yielded better performance in [29], [67]- [70], [72]- [75], [77], [79], [80], [82]- [85], [87]- [92], [94], [97], [98], [100], [101], whereas autoencoders, RNN, LSTM, and Generative Adversarial Network (GAN) formed another notable synergy [39], [71], [76], [78], [81], [86], [93], [95].…”

Section: ) Tool Detectionmentioning

confidence: 99%

Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey

et al. 2022

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The recent advancements in the surging field of Deep Learning (DL) have revolutionized every sphere of life, and the healthcare domain is no exception. The enormous success of DL models, particularly with image data, has led to the development of image-guided Robot Assisted Surgery (RAS) systems. By and large, the number of studies concerning image-driven computer assisted surgical systems using DL has increased exponentially. Additionally, the contemporary availability of surgical datasets has also boosted the DL applications in RAS. Inspired by the latest trends and contributions in surgery, this literature survey presents a summarized analysis of recent innovations of DL in image-guided RAS systems. After a thorough review, a sum of 184 articles are selected and grouped into four categories, based on the literature and the relevancy of the task in the articles, comprising 1) Surgical Tools, 2) Surgical Processes, 3) Surgical Surveillance, and 4) Surgical Performance. The survey also discusses publicly available surgical datasets and highlights the basics of the DL models. Furthermore, the legal, ethical, and technological challenges together with the intuitive predictions and recommendations related to the autonomous RAS systems are also presented. The study reveals that Convolutional Neural Network (CNN) is most widely adopted architecture, whereas, JIGSAWS is most employed dataset in RAS. The study suggests fusing kinematic data along with image data, which produces better accuracy and precision, particularly in gesture and trajectory segmentation tasks. Additionally, CNN and Long Short Term Memory (LSTM) networks have shown remarkable performance, however, authors recommend employing these gigantic architectures only when simpler models have failed to produce satisfactory results. The simpler models, despite their limitations, are time and cost effective and yield considerable outcomes even on the smaller datasets.

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Section: ) Tool Detectionmentioning

confidence: 99%

Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey

et al. 2022

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“…These works exhibit better results both in speed and accuracy. Yu et al [39] focused on the detection of small surgical instruments. They combined an attention map created from high-level features with low-level features to enrich the low semantic information.…”

Section: ) Classification and Detection Of Surgical Instrumentsmentioning

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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“…However, many challenges still need to be addressed to achieve high-precision instrument detection in endoscopic surgery, such as the blood on the instrument surface, as well as the collision of the operated surgical instruments during the surgery, let along the poor feedback on endoscopic images during the surgery which might be covered by the blur, shadow, and even reflections, further decreases the detection precision [13], [20]. Therefore, designing the effective method of instrument detection in endoscopic images to improve the precision and the safety of endoscopic surgery has become a crucial research topic [9], [20], [23], [24], [25].…”

Section: Introductionmentioning

confidence: 99%