17 November 2017 Learning to segment mouse embryo cells
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Proceedings Volume 10572, 13th International Conference on Medical Information Processing and Analysis; 1057212 (2017) https://doi.org/10.1117/12.2285967
Event: 13th International Symposium on Medical Information Processing and Analysis, 2017, San Andres Island, Colombia
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.
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Juan León, Juan León, Alejandro Pardo, Alejandro Pardo, Pablo Arbeláez, Pablo Arbeláez, } "Learning to segment mouse embryo cells", Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 1057212 (17 November 2017); doi: 10.1117/12.2285967; https://doi.org/10.1117/12.2285967
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