A Multi-Modal Learning System for On-Line Surgical Action Segmentation

Rossi, Giacomo De; Roin, Serena; Setti, Francesco; Muradore, Riccardo

doi:10.1109/ismr48331.2020.9312950

Cited by 2 publications

(2 citation statements)

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“…The surgical phase and state estimation subgroup a particular surgical process into several chunks of phases and establish a system that recognises the phase or state of the surgical process on a given set of inputs. The task is largely applied in several domains including cholecystectomy, endovascular, and esophagectomy procedures [81], [193]- [200], however, it appears to have less generalization for other types of surgical procedures.…”

Section: ) Phase and State Estimationmentioning

confidence: 99%

“…Dissimilar to the surgical planning, the availability of the data for phase recognition makes majority of the articles rely upon public datasets [81], [193], [195]- [197], [200] which include kinematic data with images and videos. The CNN and LSTM are two most frequently applied methods with accuracy being the top performance measuring parameter.…”

Section: ) Phase and State Estimationmentioning

confidence: 99%

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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: ) Phase and State Estimationmentioning

confidence: 99%