Detection of articulated instruments in retinal microsurgery

Alsheakhali, Mohamed; Eslami, Abouzar; Navab, Nassir

doi:10.1109/isbi.2016.7493222

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…Since 2017, most tool analysis solutions rely on deep learning. For tool detection, convolutional neural networks (CNNs) were used to recognize images patches containing tool pixels (Alsheakhali et al, 2016a;Chen et al, 2017;Zhao et al, 2017). The use of region proposal networks was also investigated (Sarikaya et al, 2017;Jin et al, 2018).…”

Section: Computer Vision Algorithmsmentioning

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 Algorithmsmentioning

confidence: 99%

CATARACTS: Challenge on automatic tool annotation for cataRACT surgery

Hajj¹,

Lamard²,

Conze³

et al. 2019

Medical Image Analysis

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show abstract

“…For this purpose, we motivate the use of a fully convolutional network, that models the problem of landmark localization as a regression of a set of heatmaps (one per landmark) in combination with semantic segmentation. This approach exploits global context to identify the position of the tool and has clear advantages comparing to patch-based techniques [19], which rely only on local information, thus being less robust towards false positives, e.g. specular reflections on the instrument or shadows.…”

Section: Methodsmentioning

confidence: 99%

Concurrent Segmentation and Localization for Tracking of Surgical Instruments

Laina

Rieke

Rupprecht

et al. 2017

Lecture Notes in Computer Science

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126

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Real-time instrument tracking is a crucial requirement for various computer-assisted interventions. In order to overcome problems such as specular reflections and motion blur, we propose a novel method that takes advantage of the interdependency between localization and segmentation of the surgical tool. In particular, we reformulate the 2D instrument pose estimation as heatmap regression and thereby enable a concurrent, robust and near real-time regression of both tasks via deep learning. As demonstrated by our experimental results, this modeling leads to a significantly improved performance than directly regressing the tool position and allows our method to outperform the state of the art on a Retinal Microsurgery benchmark and the MICCAI EndoVis Challenge 2015. * I. Laina and N. Rieke contributed equally to this work.

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“…Hence, once the tracker gets lost, the operation has to be interrupted to reinitialize OCT device manually. A recent work [3] proposed to use the deep learning to detect the instrument parts and estimate its orientation. The approach achieved comparable results to the state-of-the-art methods but it is computationally expensive as well as it cannot detect the two forceps tips.…”

Section: Previous Workmentioning

confidence: 99%

“…Many factors such as the cluttered background, presence of blood vessels, instrument shadow, and rapid illumination have negative impact on tracking quality. Recent approaches [2, 3] modeled the instrument as a multiparts object where the parts are connected to each other in a linear way. Such approaches do not have the ability to detect the instrument tips in case of forceps usage where the linearity condition of the parts distribution is not satisfied.…”

Section: Introductionmentioning

confidence: 99%

CRF-Based Model for Instrument Detection and Pose Estimation in Retinal Microsurgery

Alsheakhali

Eslami

Roodaki

et al. 2016

Computational and Mathematical Methods in Medicine

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Detection of instrument tip in retinal microsurgery videos is extremely challenging due to rapid motion, illumination changes, the cluttered background, and the deformable shape of the instrument. For the same reason, frequent failures in tracking add the overhead of reinitialization of the tracking. In this work, a new method is proposed to localize not only the instrument center point but also its tips and orientation without the need of manual reinitialization. Our approach models the instrument as a Conditional Random Field (CRF) where each part of the instrument is detected separately. The relations between these parts are modeled to capture the translation, rotation, and the scale changes of the instrument. The tracking is done via separate detection of instrument parts and evaluation of confidence via the modeled dependence functions. In case of low confidence feedback an automatic recovery process is performed. The algorithm is evaluated on in vivo ophthalmic surgery datasets and its performance is comparable to the state-of-the-art methods with the advantage that no manual reinitialization is needed.

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Detection of articulated instruments in retinal microsurgery

Cited by 6 publications

References 10 publications

CATARACTS: Challenge on automatic tool annotation for cataRACT surgery

CATARACTS: Challenge on automatic tool annotation for cataRACT surgery

Concurrent Segmentation and Localization for Tracking of Surgical Instruments

CRF-Based Model for Instrument Detection and Pose Estimation in Retinal Microsurgery

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