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Proceedings Paper

Learning to segment mouse embryo cells
Author(s): Juan León; Alejandro Pardo; Pablo Arbeláez
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Paper Abstract

Recent advances in microscopy enable the capture of temporal sequences during cell development stages. However, the study of such sequences is a complex task and time consuming task. In this paper we propose an automatic strategy to adders the problem of semantic and instance segmentation of mouse embryos using NYU’s Mouse Embryo Tracking Database. We obtain our instance proposals as refined predictions from the generalized hough transform, using prior knowledge of the embryo’s locations and their current cell stage. We use two main approaches to learn the priors: Hand crafted features and automatic learned features. Our strategy increases the baseline jaccard index from 0.12 up to 0.24 using hand crafted features and 0.28 by using automatic learned ones.

Paper Details

Date Published: 17 November 2017
PDF: 9 pages
Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 1057212 (17 November 2017); doi: 10.1117/12.2285967
Show Author Affiliations
Juan León, Univ. de los Andes (Colombia)
Alejandro Pardo, Univ. de los Andes (Colombia)
Pablo Arbeláez, Univ. de los Andes (Colombia)

Published in SPIE Proceedings Vol. 10572:
13th International Conference on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva; Juan David García, Editor(s)

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