Segmentation of Neuron and Measurement of Optically Programed Neurite Growth: Fast Automation via Bayesian Thresholding

Reddy, P and Shukla, S and Karunarathne, A and Jana, Soumya and Giri, Lopamudra (2015) Segmentation of Neuron and Measurement of Optically Programed Neurite Growth: Fast Automation via Bayesian Thresholding. In: 7th Annual International IEEE EMBS Conference on Neural Engineering, 22 - 24 April, 2015, Montpellier, France,.

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The variability and complex dynamics of cell morphology make the automated segmentation of neurons in microscopic images a rather difficult task. To fully leverage modern computational power in large-scale analysis of such biological images, automation is necessary. In this paper, we present an automated approach to segmenting individual cells from their surroundings, and test it on time-lapse images of hipppocampal neurons during neurite initiation and extension. Noting that active contour based methods are usually accurate, but computationally expensive and slow, we propose a fast hybrid approach that combines Chan-Vese active contour segmentation with Bayesian thresholding for segmentation of neuron and measurement of neurite growth dynamics. Our approach demonstrated upto two-hundred-fold faster quantification of growth dynamics compared to the pure Chan-Vese segmentation.

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IITH Creators:
IITH CreatorsORCiD
Giri, Lopamudra
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Bayesian thresholding, Chan-Vese active contour segmentation, fast automation, hipppocampal neurons, neurite extension, neurite growth dynamics, neurite initiation, neuron segmentation, optically programed neurite growth, time-lapse images
Subjects: Others > Electricity
Others > Electronic imaging & Singal processing
Others > Engineering technology
Chemical Engineering > Biochemical Engineering
Divisions: Department of Chemical Engineering
Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 08 Sep 2015 07:16
Last Modified: 11 Sep 2017 10:54
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