Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9956828/ |
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| author | Sardar Mehboob Hussain Antonio Brunetti Giuseppe Lucarelli Riccardo Memeo Vitoantonio Bevilacqua Domenico Buongiorno |
| author_facet | Sardar Mehboob Hussain Antonio Brunetti Giuseppe Lucarelli Riccardo Memeo Vitoantonio Bevilacqua Domenico Buongiorno |
| author_sort | Sardar Mehboob Hussain |
| collection | DOAJ |
| description | 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, the 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 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. |
| first_indexed | 2024-04-12T05:15:48Z |
| format | Article |
| id | doaj.art-4a4faec7d2764c46b4d703ead8c771cb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T05:15:48Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4a4faec7d2764c46b4d703ead8c771cb2022-12-22T03:46:37ZengIEEEIEEE Access2169-35362022-01-011012262712265710.1109/ACCESS.2022.32237049956828Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature SurveySardar Mehboob Hussain0https://orcid.org/0000-0003-2084-9880Antonio Brunetti1https://orcid.org/0000-0002-1934-0983Giuseppe Lucarelli2https://orcid.org/0000-0001-7807-1229Riccardo Memeo3Vitoantonio Bevilacqua4https://orcid.org/0000-0002-3088-0788Domenico Buongiorno5https://orcid.org/0000-0002-2024-5369Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bari, ItalyDepartment of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bari, ItalyDepartment of Emergency and Organ Transplantation, Urology, Andrology and Kidney Transplantation Unit, University of Bari, Bari, ItalyDepartment of Hepatobiliary and Pancreatic Surgery, Miulli Hospital, Bari, ItalyDepartment of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bari, ItalyDepartment of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Bari, ItalyThe 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, the 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 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.https://ieeexplore.ieee.org/document/9956828/Deep learningconvolutional neural networkminimally invasive surgerycomputer-assisted interventionrobotic surgeryrobot-assisted surgery |
| spellingShingle | Sardar Mehboob Hussain Antonio Brunetti Giuseppe Lucarelli Riccardo Memeo Vitoantonio Bevilacqua Domenico Buongiorno Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey IEEE Access Deep learning convolutional neural network minimally invasive surgery computer-assisted intervention robotic surgery robot-assisted surgery |
| title | Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey |
| title_full | Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey |
| title_fullStr | Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey |
| title_full_unstemmed | Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey |
| title_short | Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey |
| title_sort | deep learning based image processing for robot assisted surgery a systematic literature survey |
| topic | Deep learning convolutional neural network minimally invasive surgery computer-assisted intervention robotic surgery robot-assisted surgery |
| url | https://ieeexplore.ieee.org/document/9956828/ |
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