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

Author:

Guy SylvainORCID,Haberbusch Jean-LoupORCID,Promayon EmmanuelORCID,Mancini Stéphane,Voros Sandrine

Abstract

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.

Funder

Agence Nationale de la Recherche

The Carnot Network

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging

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