A Deep Learning Approach for Segmenting Time-Lapse Phase Contrast Images of NIH 3T3 Fibroblast Cells

Aruna Kumari Kakumani () and L. Padma Sree ()
Additional contact information
Aruna Kumari Kakumani: VNR Vignana Jyothi Institute of Engineering and Technology
L. Padma Sree: VNR Vignana Jyothi Institute of Engineering and Technology

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 855-862 from Springer

Abstract: Abstract Separating cells from the background in microscopy images is the critical step in image processing pipeline for the study of single cell life cycle. Live cell imaging experiments involve thousands of cells and images taken for a few days, which results in huge data generation. Automatic analysis of such images is essential rather than performing analysis manually. The challenges involved are non-uniform illumination of the image, different types of cell lines to be studied, large curation time required and analysis of large data to name a few. In this work we present a image processing pipeline using a convolutional neural network (CNN) model followed by thresholding and morphological operations for segmenting the NIH 3T3 cells in microscopic images. The segmentation results are evaluated by comparing them with the ground truth images. The proposed methodology gave a Dice index of 0.93 on a stack of 238 phase contrast images. Further, we show that CNN based approach performs superior to conventional image processing segmentation methods on phase contrast images of NIH 3T3 cells.

Keywords: Deep learning; Convolutional neural networks; Microscopy images (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_86

Ordering information: This item can be ordered from
http://www.springer.com/9783030418625

DOI: 10.1007/978-3-030-41862-5_86

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().