TY - GEN
T1 - Celeganser
T2 - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
AU - Wang, Linfeng
AU - Kong, Shu
AU - Pincus, Zachary
AU - Fowlkes, Charless
N1 - Funding Information:
The authors gratefully acknowledge Logan Tan, Nico-lette Laird and Aditya Somisetty of the Pincus Lab who created and curated the ground-truth image annotations. This research was supported by NIH grant NIA R01AG057748, NSF grants IIS-1813785 and IIS-1618806, a research gift from Qualcomm, and a hardware donation from NVIDIA. Shu Kong also acknowledges Kleist Endowed Fellowship for the generous support of inter-disciplinary research.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - The nematode Caenorhabditis elegans (C. elegans) serves as an important model organism in a wide variety of biological studies. In this paper we introduce a pipeline for automated analysis of C. elegans imagery for the purpose of studying life-span, health-span and the underlying genetic determinants of aging. Our system detects and segments the worm, and predicts body coordinates at each pixel location inside the worm. These coordinates provides dense correspondence across individual animals to allow for meaningful comparative analysis. We show that a model pre-trained to perform body-coordinate regression extracts rich features that can be used to predict the age of individual worms with high accuracy. This lays the ground for future research in quantifying the relation between organs' physiologic and biochemical state, and individual life/health-span.
AB - The nematode Caenorhabditis elegans (C. elegans) serves as an important model organism in a wide variety of biological studies. In this paper we introduce a pipeline for automated analysis of C. elegans imagery for the purpose of studying life-span, health-span and the underlying genetic determinants of aging. Our system detects and segments the worm, and predicts body coordinates at each pixel location inside the worm. These coordinates provides dense correspondence across individual animals to allow for meaningful comparative analysis. We show that a model pre-trained to perform body-coordinate regression extracts rich features that can be used to predict the age of individual worms with high accuracy. This lays the ground for future research in quantifying the relation between organs' physiologic and biochemical state, and individual life/health-span.
UR - http://www.scopus.com/inward/record.url?scp=85090130584&partnerID=8YFLogxK
U2 - 10.1109/CVPRW50498.2020.00492
DO - 10.1109/CVPRW50498.2020.00492
M3 - Conference contribution
AN - SCOPUS:85090130584
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 4164
EP - 4173
BT - Proceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
PB - IEEE Computer Society
Y2 - 14 June 2020 through 19 June 2020
ER -