4 research outputs found
SimCol3D -- 3D Reconstruction during Colonoscopy Challenge
Colorectal cancer is one of the most common cancers in the world. While
colonoscopy is an effective screening technique, navigating an endoscope
through the colon to detect polyps is challenging. A 3D map of the observed
surfaces could enhance the identification of unscreened colon tissue and serve
as a training platform. However, reconstructing the colon from video footage
remains unsolved due to numerous factors such as self-occlusion, reflective
surfaces, lack of texture, and tissue deformation that limit feature-based
methods. Learning-based approaches hold promise as robust alternatives, but
necessitate extensive datasets. By establishing a benchmark, the 2022 EndoVis
sub-challenge SimCol3D aimed to facilitate data-driven depth and pose
prediction during colonoscopy. The challenge was hosted as part of MICCAI 2022
in Singapore. Six teams from around the world and representatives from academia
and industry participated in the three sub-challenges: synthetic depth
prediction, synthetic pose prediction, and real pose prediction. This paper
describes the challenge, the submitted methods, and their results. We show that
depth prediction in virtual colonoscopy is robustly solvable, while pose
estimation remains an open research question
SimCol3D - 3D Reconstruction during Colonoscopy Challenge
Colorectal cancer is one of the most common cancers in the world. While colonoscopy is an effective screening technique, navigating an endoscope through the colon to detect polyps is challenging. A 3D map of the observed surfaces could enhance the identification of unscreened colon tissue and serve as a training platform. However, reconstructing the colon from video footage remains unsolved due to numerous factors such as self-occlusion, reflective surfaces, lack of texture, and tissue deformation that limit feature-based methods. Learning-based approaches hold promise as robust alternatives, but necessitate extensive datasets. By establishing a benchmark, the 2022 EndoVis sub-challenge SimCol3D aimed to facilitate data-driven depth and pose prediction during colonoscopy. The challenge was hosted as part of MICCAI 2022 in Singapore. Six teams from around the world and representatives from academia and industry participated in the three sub-challenges: synthetic depth prediction, synthetic pose prediction, and real pose prediction. This paper describes the challenge, the submitted methods, and their results. We show that depth prediction in virtual colonoscopy is robustly solvable, while pose estimation remains an open research question
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
Context-aware decision support in the operating room can foster surgical
safety and efficiency by leveraging real-time feedback from surgical workflow
analysis. Most existing works recognize surgical activities at a coarse-grained
level, such as phases, steps or events, leaving out fine-grained interaction
details about the surgical activity; yet those are needed for more helpful AI
assistance in the operating room. Recognizing surgical actions as triplets of
combination delivers comprehensive details about the
activities taking place in surgical videos. This paper presents
CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for
the recognition of surgical action triplets in laparoscopic videos. The
challenge granted private access to the large-scale CholecT50 dataset, which is
annotated with action triplet information. In this paper, we present the
challenge setup and assessment of the state-of-the-art deep learning methods
proposed by the participants during the challenge. A total of 4 baseline
methods from the challenge organizers and 19 new deep learning algorithms by
competing teams are presented to recognize surgical action triplets directly
from surgical videos, achieving mean average precision (mAP) ranging from 4.2%
to 38.1%. This study also analyzes the significance of the results obtained by
the presented approaches, performs a thorough methodological comparison between
them, in-depth result analysis, and proposes a novel ensemble method for
enhanced recognition. Our analysis shows that surgical workflow analysis is not
yet solved, and also highlights interesting directions for future research on
fine-grained surgical activity recognition which is of utmost importance for
the development of AI in surgery.Comment: CholecTriplet2021 challenge report. Submitted to journal of Medical
Image Analysis. 22 pages, 8 figures, 11 table