English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Minimally invasive surgery is highly operator dependant with a lengthyprocedural time causing fatigue to surgeon and risks to patients such as injuryto organs, infection, bleeding, and complications of anesthesia. To mitigatesuch risks, real-time systems are desired to be developed that can provideintra-operative guidance to surgeons. For example, an automated system for toollocalization, tool (or tissue) tracking, and depth estimation can enable aclear understanding of surgical scenes preventing miscalculations duringsurgical procedures. In this work, we present a systematic review of recentmachine learning-based approaches including surgical tool localization,segmentation, tracking, and 3D scene perception. Furthermore, we provide adetailed overview of publicly available benchmark datasets widely used forsurgical navigation tasks. While recent deep learning architectures have shownpromising results, there are still several open research problems such as alack of annotated datasets, the presence of artifacts in surgical scenes, andnon-textured surfaces that hinder 3D reconstruction of the anatomicalstructures. Based on our comprehensive review, we present a discussion oncurrent gaps and needed steps to improve the adaptation of technology insurgery.

A comprehensive survey on recent deep learning-based methods applied to surgical data