SPST-CNN: Spatial pyramid based searching and tagging of liver’s intraoperative live views via CNN for minimal invasive surgery

Nazir, Abdul; Cheema, Muhammad Nadeem; Sheng, Bin; Li, Ping; Li, Huating; Yang, Po; Jung, Younhyun; Qin, Jing; Feng, David Dagan

doi:10.1016/j.jbi.2020.103430

Cited by 14 publications

(8 citation statements)

References 25 publications

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“…The vessel detection and needle insertion are prominent and attractive applications, and surgical robots are made to achieve timely fashioned aid [184]. The recent trends in surgical planning have shown great interest in the 2D, 3D, and 4D model construction for the interventional guidance [184]- [192]. Due to the unavailability of the large amount of data for preoperative planning, majority of the articles rely upon in-house data [185]- [188], [190]- [192] which includes CT scans and MRI scans [185]- [187], [189], [190].…”

Section: ) Surgical Planningmentioning

confidence: 99%

“…The recent trends in surgical planning have shown great interest in the 2D, 3D, and 4D model construction for the interventional guidance [184]- [192]. Due to the unavailability of the large amount of data for preoperative planning, majority of the articles rely upon in-house data [185]- [188], [190]- [192] which includes CT scans and MRI scans [185]- [187], [189], [190]. As can be seen in the Table 7, the majority of the authors use Dice performance measure.…”

Section: ) Surgical Planningmentioning

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: ) Surgical Planningmentioning

confidence: 99%

Section: ) Surgical Planningmentioning

confidence: 99%

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

et al. 2022

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“…Liver segmentation is a another particularly challenging task as this organ suffers many deformations during a surgery, and it is usually overlapped by other organs. Nazir et al [54] proposed a method to search a part of the liver view from its respective full view at runtime. The idea is to construct an image pyramid using different sizes from a full view input image for scaleinvariance.…”

Section: ) Segmentation Of Anatomical Structuresmentioning

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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“…Nazir et al 62 reported a new searching and tagging system that recognizes various anatomical landmarks in laparoscopic liver surgery. Only one article focused on intraoperative navigation with AI in liver surgery from 2020–2021.…”

Section: Intraoperative Navigationmentioning

confidence: 99%

Essential updates 2020/2021: Current topics of simulation and navigation in hepatectomy

Saito

Shimada

Morine

et al. 2021

Annals of Gastroent Surgery

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Advances in perioperative care and surgical techniques have significantly improved the outcomes of liver resection during the last three decades. Liver surgery has inherent challenges, including difficult anticipation of complex and variable intrahepatic anatomy and the need for cognitive analysis by the surgeon to integrate preoperative imaging information into the operative field. Therefore, simulation and navigation techniques have been developed in this field.In preoperative simulation, three-dimensional (3D) simulation technology was developed in Germany in the early 2000s, 1 and immediately thereafter software based on an original algorithm was developed in Japan. The development of intraoperative navigation techniques may also help surgeons to perform liver resections as planned. Intraoperative navigation began with intraoperative ultrasound (US) in 1980 2 and progressed to virtual hepatectomy (Hx), [3][4][5][6][7] real-time virtual sonography, 8-12 and finally indocyanine green (ICG) fluorescence. [13][14][15][16][17][18][19][20] Thus, intraoperative navigation has gradually evolved during the past 40 y. This biannual review discusses the essential updates to simulation and navigation in Hx that occurred in the 2-y period from 2020 to 2021. | PREOPER ATIVE S IMUL ATIONPreoperative 3D simulation has enabled surgeons to obtain a great deal of information, such as detailed anatomical visualization, the

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SPST-CNN: Spatial pyramid based searching and tagging of liver’s intraoperative live views via CNN for minimal invasive surgery

Cited by 14 publications

References 25 publications

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

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

A Review on Deep Learning in Minimally Invasive Surgery

Essential updates 2020/2021: Current topics of simulation and navigation in hepatectomy

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