Surgical Vision

Stoyanov, Danail

doi:10.1007/s10439-011-0441-z

Cited by 89 publications

(77 citation statements)

References 133 publications

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“…However, 3D surface reconstruction of surgical endoscopic images is still an active area of research due to some challenging aspects that include: (i) the application in surgery requiring high accuracy and robustness in order to ensure patient safety; (ii) di culties arising due to large lens distortion, many texture-less areas, occlusions introduced by the surgical tools, specular highlights, smoke and blood produced during the intervention [25]; etc. Many approaches have been proposed in literature in order to achieve more reliable, robust, and real-time depth measurement to represent a deforming environment.…”

Section: State Of the Artmentioning

confidence: 99%

Dense soft tissue 3D reconstruction refined with super-pixel segmentation for robotic abdominal surgery

et al. 2015

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Purpose: Single Incision Laparoscopic Surgery (SILS) decreases post-operative infections, but introduces limitations in the surgeon's manoeuverability and in the surgical field of view. This work aims at enhancing intraoperative surgical visualization by exploiting the 3D information about the surgical site. An interactive guidance system is proposed wherein the pose of pre-operative tissue models is updated online. A critical process involves the intraoperative acquisition of tissue surfaces. It can be achieved using stereoscopic imaging and 3D reconstruction techniques. This work contributes to this process by proposing new methods for improved dense 3D reconstruction of soft tissues, which allows a more accurate deformation identification and facilitates the registration process.Methods: Two methods for soft tissue 3D reconstruction are proposed: Method 1 follows the traditional approach of the block matching algorithm. Method 2 performs a non-parametric Modified Census Transform to be more robust to illumination variation. The Simple Linear Iterative Clustering (SLIC) super pixel al- gorithm is exploited for disparity refinement by filling holes in the disparity images.Results: The methods were validated using two video datasets from the Hamlyn Centre, achieving an accuracy of 2.95 mm and 1.66 mm respectively. A comparison with ground truth data demonstrated the disparity refinement procedure: (i) increases the number of reconstructed points by up to 43%; (ii) does not a↵ect the accuracy of the 3D reconstructions significantly.Conclusion: Both methods give results that compare favourably with the state-of-the-art methods. The computational time constraints their applicability in realtime, but can be greatly improved by using a GPU implementation.

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Section: State Of the Artmentioning

confidence: 99%

Dense soft tissue 3D reconstruction refined with super-pixel segmentation for robotic abdominal surgery

et al. 2015

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“…9). Measuring 3D tissue surface shape and motion during surgery has important implications for emerging biophotonics modalities (Stoyanov, 2012b). Often biophotonics techniques have physical limitations that impede the practical FoV and the in vivo imaging of tissue that may be undergoing physiological motion.…”

Section: Biophotonicsmentioning

confidence: 99%

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

Maier-Hein

Mountney

Bartoli

et al. 2013

Medical Image Analysis

242

130

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One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon's navigation capabilites by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D optical imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions.

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“…Numerous advantages of minimally invasive surgeries (MIS) [1] over conventional (open) ones, beside the self-evident less invasiveness, include much less postoperative pain and blood loss, minor scaring and shorter recovery time, which make them lucrative for both inpatients and clinicians. However, the indirect method of observation and manipulation in MIS complicates the depth perception and impairs the eyehand coordination of surgeons, which necessitates their acquisition of additional information to monitor the operational instruments moving within the body.…”

Section: Introductionmentioning

confidence: 99%

Tracking-by-detection of surgical instruments in minimally invasive surgery via the convolutional neural network deep learning-based method

Zhao

Voros

Weng

et al. 2017

Computer Assisted Surgery

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Background: Worldwide propagation of minimally invasive surgeries (MIS) is hindered by their drawback of indirect observation and manipulation, while monitoring of surgical instruments moving in the operated body required by surgeons is a challenging problem. Tracking of surgical instruments by vision-based methods is quite lucrative, due to its flexible implementation via software-based control with no need to modify instruments or surgical workflow. Methods: A MIS instrument is conventionally split into a shaft and end-effector portions, while a 2D/3D tracking-by-detection framework is proposed, which performs the shaft tracking followed by the end-effector one. The former portion is described by line features via the RANSAC scheme, while the latter is depicted by special image features based on deep learning through a well-trained convolutional neural network. Results: The method verification in 2D and 3D formulation is performed through the experiments on ex-vivo video sequences, while qualitative validation on in-vivo video sequences is obtained. Conclusion: The proposed method provides robust and accurate tracking, which is confirmed by the experimental results: its 3D performance in ex-vivo video sequences exceeds those of the available state-of -the-art methods. Moreover, the experiments on in-vivo sequences demonstrate that the proposed method can tackle the difficult condition of tracking with unknown camera parameters. Further refinements of the method will refer to the occlusion and multi-instrumental MIS applications.

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Surgical Vision

Cited by 89 publications

References 133 publications

Dense soft tissue 3D reconstruction refined with super-pixel segmentation for robotic abdominal surgery

Dense soft tissue 3D reconstruction refined with super-pixel segmentation for robotic abdominal surgery

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

Tracking-by-detection of surgical instruments in minimally invasive surgery via the convolutional neural network deep learning-based method

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