Augmented 3-D View for Laparoscopy Surgery

Tamadazte, Brahim; Voros, Nikolaos S.; Boschet, Christophe; Cinquin, Philippe; Fouard, Céline

doi:10.1007/978-3-642-38085-3_12

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…This would, however, require a high-quality segmentation. Another alternative could be to rely on the 3D reconstruction of the scene [ 7 ], at the expense of high computational resources. Concerning the promising neural network approaches, such as UDIS, the straightforward approach to improve them for laparoscopy would be to use them with a huge unlabelled dataset from our simulated laparoscopic environment.…”

Section: Discussionmentioning

confidence: 99%

“…In [ 6 ], a modified trocar with two miniature cameras was evaluated through animal experiments. Preliminary works have shown a reduction in operating time, in the number of commands of a robotic endoscope holder [ 7 ], and a faster reaction to adverse events [ 8 ] in a testbench environment. In [ 9 ], a trocar–camera assembly deployed four miniaturised cameras, increasing the FoV of a 10 mm endoscope by 51%.…”

Section: Introductionmentioning

confidence: 99%

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Qualitative Comparison of Image Stitching Algorithms for Multi-Camera Systems in Laparoscopy

et al. 2022

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Multi-camera systems were recently introduced into laparoscopy to increase the narrow field of view of the surgeon. The video streams are stitched together to create a panorama that is easier for the surgeon to comprehend. Multi-camera prototypes for laparoscopy use quite basic algorithms and have only been evaluated on simple laparoscopic scenarios. The more recent state-of-the-art algorithms, mainly designed for the smartphone industry, have not yet been evaluated in laparoscopic conditions. We developed a simulated environment to generate a dataset of multi-view images displaying a wide range of laparoscopic situations, which is adaptable to any multi-camera system. We evaluated classical and state-of-the-art image stitching techniques used in non-medical applications on this dataset, including one unsupervised deep learning approach. We show that classical techniques that use global homography fail to provide a clinically satisfactory rendering and that even the most recent techniques, despite providing high quality panorama images in non-medical situations, may suffer from poor alignment or severe distortions in simulated laparoscopic scenarios. We highlight the main advantages and flaws of each algorithm within a laparoscopic context, identify the main remaining challenges that are specific to laparoscopy, and propose methods to improve these approaches. We provide public access to the simulated environment and dataset.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Qualitative Comparison of Image Stitching Algorithms for Multi-Camera Systems in Laparoscopy

et al. 2022

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“…Surface rendering techniques in [98] depict the conversion of anatomical structures into a mesh for delineation and segmentation. Tamadazte et al [99] used the epipolar geometry principle to acquire images from the left and right stereovision cameras. The authors then used a point correspondence approach to resample and build a 3D triangular mesh from local data points in its neighborhood.…”

Section: Surface Rendering After Segmentation Of Pre-processed Datamentioning

confidence: 99%

Augmented Reality (AR) for Surgical Robotic and Autonomous Systems: State of the Art, Challenges, and Solutions

Seetohul

Shafiee

Sirlantzis

2023

Sensors

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Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human–robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future.

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