A Learning Approach for Suture Thread Detection With Feature Enhancement and Segmentation for 3-D Shape Reconstruction

Lu, Bo; Yu, Xiaoqing; Lai, Jun; Huang, Kaicheng; Chan, Keith C. C.; Chu, Henry K.

doi:10.1109/tase.2019.2950005

Cited by 20 publications

(11 citation statements)

References 41 publications

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“…Although for cables, and DLOs in general, the literature concerning perception algorithms is quite reduced, this is not the case for other domains where the treated objects may have some similarities with cables, such as vessel [21] and suture threads [22] segmentation in medical images. In fact, DLOs appear as tubular structure in images.…”

Section: Related Workmentioning

confidence: 99%

“…Regarding the medical domain, in [21] the authors propose a new curvilinear structure segmentation network and show their application to vessel segmentation task. In [22] an approach consisting on the identification of the suture thread tips and a novel "marching" algorithm is presented aiming at segmenting the thread from the background.…”

Section: Related Workmentioning

confidence: 99%

“…Fig.10: Examples of Ariadne+ applied to plastic hoses and suture threads. The latter were provided by[22].…”

mentioning

confidence: 99%

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Ariadne+: Deep Learning--Based Augmented Framework for the Instance Segmentation of Wires

Caporali

Zanella

Gregorio³

et al. 2022

IEEE Trans. Ind. Inf.

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In this paper an innovative algorithm for instance segmentation of wires called Ariadne+ is presented. Although vastly present in many manufacturing environments, the perception and manipulation of wires is still an open problem for robotic applications. Wires are Deformable Linear Objects (DLOs) lacking of any specific shape, color and feature. The proposed approach uses deep learning and standard computer vision techniques aiming at their reliable and time effective instance segmentation. A deep convolutional neural network is employed to generate a binary mask showing where wires are present in the input image, then graph theory is applied to create the wire paths from the binary mask through an iterative approach that aims to maximize the graph coverage. In addition, the B-Spline model of each instance, useful in manipulation tasks, is provided. The approach has been validated quantitatively and qualitatively using a manually labelled test dataset and by comparing it against the original Ariadne algorithm. The timings performances of the approach have been also analyzed in depth.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Ariadne+: Deep Learning--Based Augmented Framework for the Instance Segmentation of Wires

Caporali

Zanella

Gregorio³

et al. 2022

IEEE Trans. Ind. Inf.

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“…Hu et al [27] proposed a multi-stage framework for suture segmentation based on predicting directly the curvilinear structural information of the thread, instead of modeling the task. Similarly, Lu et al [28] addressed the problem of suture thread segmentation. They proposed a DL model for accurately detect the suture's tip.…”

Section: A Surgical Image Analysismentioning

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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“…While previous approaches to suture thread localization have attempted to achieve markerless detection [13], [14], a vast majority of previous approaches to suture needle localization still rely on markers or color segmentation methods [15]- [20]. These approaches require the needle to be painted beforehand or to be always visible as well as distinguishable in the environment, making them not applicable in real-world surgeries.…”

Section: Introductionmentioning

confidence: 99%

Markerless Suture Needle 6D Pose Tracking with Robust Uncertainty Estimation for Autonomous Minimally Invasive Robotic Surgery

Chiu¹,

Liao²,

Richter³

et al. 2021

Preprint

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Suture needle localization plays a crucial role towards autonomous suturing. To track the 6D pose of a suture needle robustly, previous approaches usually add markers on the needle or perform complex operations for feature extraction, making these methods difficult to be applicable to real-world environments. Therefore in this work, we present a novel approach for markerless suture needle pose tracking using Bayesian filters. A data-efficient feature point detector is trained to extract the feature points on the needle. Then based on these detections, we propose a novel observation model that measures the overlap between the detections and the expected projection of the needle, which can be calculated efficiently. In addition, for the proposed method, we derive the approximation for the covariance of the observation noise, making this model more robust to the uncertainty in the detections. The experimental results in simulation show that the proposed observation model achieves low tracking errors of approximately 1.5mm in position in space and 1 degree in orientation. We also demonstrate the qualitative results of our trained markerless feature detector combined with the proposed observation model in real-world environments. The results show high consistency between the projection of the tracked pose and that of the real pose.

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A Learning Approach for Suture Thread Detection With Feature Enhancement and Segmentation for 3-D Shape Reconstruction

Cited by 20 publications

References 41 publications

Ariadne+: Deep Learning--Based Augmented Framework for the Instance Segmentation of Wires

Ariadne+: Deep Learning--Based Augmented Framework for the Instance Segmentation of Wires

A Review on Deep Learning in Minimally Invasive Surgery

Markerless Suture Needle 6D Pose Tracking with Robust Uncertainty Estimation for Autonomous Minimally Invasive Robotic Surgery

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