Needle Segmentation in Volumetric Optical Coherence Tomography Images for Ophthalmic Microsurgery

Zhou, Mingchuan; Roodaki, Hessam; Eslami, Abouzar; Chen, Guang; Huang, Kai; Maier, Mathias; Lohmann, Chris P.; Knoll, Alois; Nasseri, M. Ali

doi:10.3390/app7080748

Cited by 25 publications

(13 citation statements)

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“…These tasks can be categorized according the precision of the desired outputs. The finest task is tool segmentation (Bodenstedt et al, 2016 Feb-Mar;García-Peraza-Herrera et al, 2016Attia et al, 2017;Lee et al, 2017b;Zhou et al, 2017;Ross et al, 2018;Su et al, 2018). This includes multi-label tool segmentation for articulated tools (Laina et al, 2017): each tool part is associated with one label.…”

Section: Computer Vision Tasksmentioning

confidence: 99%

CATARACTS: Challenge on automatic tool annotation for cataRACT surgery

Hajj¹,

Lamard²,

Conze³

et al. 2019

Medical Image Analysis

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Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries. However, with 19 million interventions per year, the most common surgical procedure in the world is cataract surgery. The CATARACTS challenge was organized in 2017 to evaluate tool annotation algorithms in the specific context of cataract surgery. It relies on more than nine hours of videos, from 50 cataract surgeries, in which the presence of 21 surgical tools was manually annotated by two experts. With 14 participating teams, this challenge can be considered a success. As might be expected, the submitted solutions are based on deep learning. This paper thoroughly evaluates these solutions: in particular, the quality of their annotations are compared to that of human interpretations. Next, lessons learnt from the differential analysis of these solutions are discussed. We expect that they will guide the design of efficient surgery monitoring tools in the near future.

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Section: Computer Vision Tasksmentioning

confidence: 99%

CATARACTS: Challenge on automatic tool annotation for cataRACT surgery

Hajj¹,

Lamard²,

Conze³

et al. 2019

Medical Image Analysis

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“…The resulting system allowed the surgeon to perform precise and comfortable micromanipulation. More recently, sub-retinal depth-tracking of a needle using OCT has been performed [ 11 ].…”

Section: Advanced Surgical Robotic Systemsmentioning

confidence: 99%

Advanced robotic surgical systems in ophthalmology

Gerber

Pettenkofer

Hubschman

2020

Eye

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In this paper, an overview of advanced robotic surgical systems in ophthalmology is provided. The systems are introduced as representative examples of the degree of human vs. robotic control during surgical procedures. The details are presented on each system and the latest advancements of each are described. Future potential applications for surgical robotics in ophthalmology are discussed in detail, with representative examples provided alongside recent progress.

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“…3(b)). A first step towards instrument tracking in this type of data was recently proposed by Zhou et al [12] in terms of instrument segmentation based on a fully convolution network. The method however requires a volumetric dataset, is not applicable to real-time applications and is restricted to static instruments.…”

Section: Introduction and Related Workmentioning

confidence: 99%