<scp>Intraoperative</scp> surgery room management: A deep learning perspective

Tanzi, Leonardo; Piazzolla, Pietro; Vezzetti, Enrico

doi:10.1002/rcs.2136

Cited by 18 publications

(9 citation statements)

References 67 publications

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“…Although this problem has been studied for many years, the advent of DL gave a renewed push to find a solution, as well as it fostered the research in other fields of application, such as the medical 14 , 15 , 16 or face recognition 17 , 18 domains. Old pose estimation methods were based on geometrical approaches, as for example, Feature‐based methods, 19 which tried to establish correspondences between 3D models and 2D images of objects by using manually annotated local features.…”

Section: Related Workmentioning

confidence: 99%

A deep learning framework for real‐time 3D model registration in robot‐assisted laparoscopic surgery

Padovan

Marullo

Tanzi

et al. 2022

Robotics Computer Surgery

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Introduction The current study presents a deep learning framework to determine, in real‐time, position and rotation of a target organ from an endoscopic video. These inferred data are used to overlay the 3D model of patient's organ over its real counterpart. The resulting augmented video flow is streamed back to the surgeon as a support during laparoscopic robot‐assisted procedures. Methods This framework exploits semantic segmentation and, thereafter, two techniques, based on Convolutional Neural Networks and motion analysis, were used to infer the rotation. Results The segmentation shows optimal accuracies, with a mean IoU score greater than 80% in all tests. Different performance levels are obtained for rotation, depending on the surgical procedure. Discussion Even if the presented methodology has various degrees of precision depending on the testing scenario, this work sets the first step for the adoption of deep learning and augmented reality to generalise the automatic registration process.

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Section: Related Workmentioning

confidence: 99%

A deep learning framework for real‐time 3D model registration in robot‐assisted laparoscopic surgery

Padovan

Marullo

Tanzi

et al. 2022

Robotics Computer Surgery

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“…Unfortunately, in intra-operative surgery, a joint utilization of DL and AR is still underrated, due to the difficult compromise between working with 3D data and the efficiency needed for real-time elaboration. In a recent paper [ 16 ], we outlined a definition of an Intelligent Operating Room (IOR), a collaborative operating room based on highly intuitive, natural and multimodal interaction. In this study, we moved the first step in this context, as we aimed to demonstrate how to apply DL to improve the performances of a previously proposed method from our group [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance

et al. 2021

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Purpose The current study aimed to propose a Deep Learning (DL) and Augmented Reality (AR) based solution for a in-vivo robot-assisted radical prostatectomy (RARP), to improve the precision of a published work from our group. We implemented a two-steps automatic system to align a 3D virtual ad-hoc model of a patient’s organ with its 2D endoscopic image, to assist surgeons during the procedure. Methods This approach was carried out using a Convolutional Neural Network (CNN) based structure for semantic segmentation and a subsequent elaboration of the obtained output, which produced the needed parameters for attaching the 3D model. We used a dataset obtained from 5 endoscopic videos (A, B, C, D, E), selected and tagged by our team’s specialists. We then evaluated the most performing couple of segmentation architecture and neural network and tested the overlay performances. Results U-Net stood out as the most effecting architectures for segmentation. ResNet and MobileNet obtained similar Intersection over Unit (IoU) results but MobileNet was able to elaborate almost twice operations per seconds. This segmentation technique outperformed the results from the former work, obtaining an average IoU for the catheter of 0.894 (σ = 0.076) compared to 0.339 (σ = 0.195). This modifications lead to an improvement also in the 3D overlay performances, in particular in the Euclidean Distance between the predicted and actual model’s anchor point, from 12.569 (σ= 4.456) to 4.160 (σ = 1.448) and in the Geodesic Distance between the predicted and actual model’s rotations, from 0.266 (σ = 0.131) to 0.169 (σ = 0.073). Conclusion This work is a further step through the adoption of DL and AR in the surgery domain. In future works, we will overcome the limits of this approach and finally improve every step of the surgical procedure.

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“…Intraoperative surgical imaging is a growing field in molecular imaging [ 1 ] and has significantly evolved with the emergence of new techniques such as fluorescence, Raman, photoacoustic and radio-guided techniques, and, lately, deep-learning methods have also been developed and adapted to this purpose [ 2 , 3 ]. It is normally used during surgical interventions for oncological patients for localizing regions of interest such as tumors or ganglia.…”

Section: Introductionmentioning

confidence: 99%

Gamma Camera Imaging with Rotating Multi-Pinhole Collimator. A Monte Carlo Feasibility Study

Ilisie

Moliner

Morera

et al. 2021

Sensors

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In this work, we propose and analyze a new concept of gamma ray imaging that corresponds to a gamma camera with a mobile collimator, which can be used in vivo, during surgical interventions for oncological patients for localizing regions of interest such as tumors or ganglia. The benefits are a much higher sensitivity, better image quality and, consequently, a dose reduction for the patient and medical staff. This novel approach is a practical solution to the overlapping problem which is inherent to multi-pinhole gamma camera imaging and single photon emission computed tomography and which translates into artifacts and/or image truncation in the final reconstructed image. The key concept consists in introducing a relative motion between the collimator and the detector. Moreover, this design could also be incorporated into most commercially available gamma camera devices, without any excessive additional requirements. We use Monte Carlo simulations to assess the feasibility of such a device, analyze three possible designs and compare their sensitivity, resolution and uniformity. We propose a final design of a gamma camera with a high sensitivity ranging from 0.001 to 0.006 cps/Bq, and a high resolution of 0.5–1.0 cm (FWHM), for source-to-detector distances of 4–10 cm. Additionally, this planar gamma camera provides information about the depth of source (with approximate resolution of 1.5 cm) and excellent image uniformity.

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Intraoperative surgery room management: A deep learning perspective

Cited by 18 publications

References 67 publications

A deep learning framework for real‐time 3D model registration in robot‐assisted laparoscopic surgery

A deep learning framework for real‐time 3D model registration in robot‐assisted laparoscopic surgery

Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance

Gamma Camera Imaging with Rotating Multi-Pinhole Collimator. A Monte Carlo Feasibility Study

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