Development of a surgical stereo endoscopic image dataset for validating 3D stereo reconstruction algorithms

In the last decades, endoscopic stereo images have been exploited to retrieve tissue surface information of the surgical site using 3D reconstruction algorithms. The application of such algorithms in Computer Assisted Surgery (CAS) tools for Minimally Invasive Surgery (MIS) requires a robust validation process in order to guarantee reliability and safety. 3D reconstruction algorithms are commonly evaluated comparing their result with respect to a reference Ground Truth (GT). However, few datasets providing endoscopic images and GT are openly available. Considering the increasing necessity of surgical datasets, the aim of this work is the generation of an Endoscopic Abdominal Stereo (EndoAbS) dataset composed of stereo-images with associated GT for 3D stereo-reconstruction algorithm validation. To recreate the surgical scenario, a polyurethane surgical phantom abdomen was built. Images were captured with a stereo-endoscope, while for acquiring the GT a laser scanner (calibrated with respect to the stereoendoscope) was used. This dataset is openly available on-line for the benefit of the CAS community

Crescimento em diâmetro de três espécies da floresta tropical seca no nordeste do Brasil.

Editores técnicos: Marcílio José Thomazini, Elenice Fritzsons, Patrícia Raquel Silva, Guilherme Schnell e Schuhli, Denise Jeton Cardoso, Luziane Franciscon. EVINCI. Resumos

Virtual Assistive System for Robotic Single Incision Laparoscopic Surgery

Single Incision Laparoscopic Surgery (SILS) reduces the trauma of large wounds decreasing the post-operative infections, but introduces technical difficulties for the surgeon, who has to deal with at least three instruments in a single incision. These drawbacks can be overcome with the introduction of robotic arms inside the abdominal cavity, but still remain difficulties in the surgical field vision, limited by the endoscope field of view. This work is aimed at developing a system to improve the information required by the surgeon and enhance the vision during a robotic SILS. In the pre-operative phase, the segmentation and surface rendering of organs allow the surgeon to plan the surgery. During the intra-operative phase, the run-time information (tools and endoscope pose) and the pre-operative information (3D models of organs) are combined in a virtual environment. A point-based rigid registration of the virtual abdomen on the real patient creates a connection between reality and virtuality. The camera-image plane calibration allows to know at run-time the pose of the endoscopic view. The results show how using a small set of 4 points (the minimal number of points that would be used in a real procedure) for the camera-image plane calibration and for the registration between real and virtual model of the abdomen, is enough to provide a calibration/registration accuracy within the requirements

Crescimento de Bowdichia virgilioides (Sucupira-preta) no Cerrado.

Resumo

Label-based Optimization of Dense Disparity Estimation for Robotic Single Incision Abdominal Surgery

Minimally invasive surgical techniques have led to novel approaches such as Single Incision Laparoscopic Surgery (SILS), which allows the reduction of post-operative infections and patient recovery time, improving surgical outcomes. However, the new techniques pose also new challenges to surgeons: during SILS, visualization of the surgical field is limited by the endoscope field of view, and the access to the target area is limited by the fact that instruments have to be inserted through a single port. In this context, intra-operative navigation and augmented reality based on pre-operative images have the potential to enhance SILS procedures by providing the information necessary to increase the intervention accuracy and safety. Problems arise when structures of interest change their pose or deform with respect to pre-operative planning, as usually happens in soft tissue abdominal surgery. This requires online estimation of the deformations to correct the pre-operative plan, which can be done, for example, through methods of depth estimation from stereo endoscopic images (3D reconstruction). The denser the reconstruction, the more accurate the deformation identification can be. This work presents an algorithm for 3D reconstruction of soft tissue, focusing on the refinement of the disparity map in order to obtain an accurate and dense point map. This algorithm is part of an assistive system for intra-operative guidance and safety supervision for robotic abdominal SILS . Results show that comparing our method with state-of-the-art CPU implementations, the percentage of valid pixel obtained with our method is 24% higher while providing comparable accuracy. Future research will focus on the development of a real-time implementation of the proposed algorithm, potentially based on a hybrid CPU-GPU processing framework

Incremento diamétrico de Tabebuia aurea e Qualea parviflora, nativas do Cerrado.

Resumo

EndoAbS dataset: Endoscopic abdominal stereo image dataset for benchmarking 3D stereo reconstruction algorithms

Background: 3D reconstruction algorithms are of fundamental importance for augmented reality applications in computer-assisted surgery. However, few datasets of endoscopic stereo images with associated 3D surface references are currently openly available, preventing the proper validation of such algorithms. This work presents a new and rich dataset of endoscopic stereo images (EndoAbS dataset). Methods: The dataset includes (i) endoscopic stereo images of phantom abdominal organs, (ii) a 3D organ surface reference (RF) generated with a laser scanner and (iii) camera calibration parameters. A detailed description of the generation of the phantom and the camera–laser calibration method is also provided. Results: An estimation of the overall error in creation of the dataset is reported (camera–laser calibration error 0.43 mm) and the performance of a 3D reconstruction algorithm is evaluated using EndoAbS, resulting in an accuracy error in accordance with state-of-the-art results (<2 mm). Conclusions: The EndoAbS dataset contributes to an increase the number and variety of openly available datasets of surgical stereo images, including a highly accurate RF and different surgical conditions

EndoAbS dataset: Endoscopic abdominal stereo image dataset for benchmarking 3D stereo reconstruction algorithms

none5siembargoed_20190801Penza, Veronica; Ciullo, Andrea S.; Moccia, Sara; Mattos, Leonardo S.; De Momi, ElenaPenza, Veronica; Ciullo, Andrea S.; Moccia, Sara; Mattos, Leonardo S.; De Momi, Elen