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CNN-Based Preprocessing to Optimize Watershed-Based Cell Segmentation in 3D Confocal Microscopy Images

Eschweiler, Dennis and Spina, Thiago V. and Choudhury, Rohan C. and Meyerowitz, Elliot and Cunha, Alexandre and Stegmaier, Johannes (2019) CNN-Based Preprocessing to Optimize Watershed-Based Cell Segmentation in 3D Confocal Microscopy Images. In: 2019 IEEE 16th International Symposium on Biomedical Imaging. IEEE , Piscataway, NJ, pp. 223-227. ISBN 978-1-5386-3640-4. http://resolver.caltech.edu/CaltechAUTHORS:20190718-132802709

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Abstract

The quantitative analysis of cellular membranes helps understanding developmental processes at the cellular level. Particularly 3D microscopic image data offers valuable insights into cell dynamics, but error-free automatic segmentation remains challenging due to the huge amount of data generated and strong variations in image intensities. In this paper, we propose a new 3D segmentation approach that combines the discriminative power of convolutional neural networks (CNNs) for preprocessing and investigates the performance of three watershed-based postprocessing strategies (WS), which are well suited to segment object shapes, even when supplied with vague seed and boundary constraints. To leverage the full potential of the watershed algorithm, the multi-instance segmentation problem is initially interpreted as three-class semantic segmentation problem, which in turn is well-suited for the application of CNNs. Using manually annotated 3D confocal microscopy images of Arabidopsis thaliana, we show the superior performance of the proposed method compared to the state of the art.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/ISBI.2019.8759242DOIArticle
ORCID:
AuthorORCID
Meyerowitz, Elliot0000-0003-4798-5153
Stegmaier, Johannes0000-0003-4072-3759
Additional Information:© 2019 IEEE.
Subject Keywords:Watershed, CNN, Multi-Instance, Cell Segmentation, Developmental Biology, 3D Image Analysis
Record Number:CaltechAUTHORS:20190718-132802709
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190718-132802709
Official Citation:D. Eschweiler, T. V. Spina, R. C. Choudhury, E. Meyerowitz, A. Cunha and J. Stegmaier, "CNN-Based Preprocessing to Optimize Watershed-Based Cell Segmentation in 3D Confocal Microscopy Images," 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, 2019, pp. 223-227. doi: 10.1109/ISBI.2019.8759242
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:97236
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:18 Jul 2019 20:50
Last Modified:18 Jul 2019 20:50

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