Other Open Access
Stefanie Speidel; Lena Maier-Hein; Danail Stoyanov; Sebastian Bodenstedt; Martin Wagner; Beat Müller; Jonathan Chen; Benjamin Müller; Franziska Mathis-Ullrich; Paul Scheikl; Jorge Bernal; Aymeric Histache; Gloria Fernandes-Esparrach; Xavier Dray; Sophia Bano; Alessandro Casella; Francisco Vasconcelos; Sara Moccia; Chinedu Nwoye; Deepak Alapatt; Armine Vardazaryan; Nicolas Padoy; Arnaud Huaulme; Kanako Harada; Pierre Jannin; Aneeq Zia; Kiran Bhattacharyya; Xi Liu; Ziheng Wang; Anthony Jarc
Minimally invasive surgery using cameras to observe the internal anatomy is the preferred approach to many surgical procedures. Furthermore, other surgical disciplines rely on microscopic images or use flexible endoscopes for diagnostic purposes. As a result, endoscopic and microscopic image processing as well as surgical vision are evolving as techniques needed to facilitate computer assisted interventions (CAI). Algorithms that have been reported for such images include 3D surface reconstruction, salient feature motion tracking, instrument detection or activity recognition. However, what is missing so far are common datasets for consistent evaluation and benchmarking of algorithms against each other.
As a vision CAI challenge at MICCAI, our aim is to provide a formal framework for evaluating the current state of the art, gather researchers in the field and provide high quality data with protocols for validating endoscopic vision algorithms. EndoVis serves as an umbrella for different kinds of sub-challenges that tackle specific problems and applications in endoscopic/microsopic vision.