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Computer Assisted Analysis of the Hepatic Spheroid Formation

Conference paper
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Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 11)

Abstract

Computer assisted techniques could enable the use of morphological characteristics of hepatic spheroids as surrogate for their response to various stimuli. The aim of this work is to develop an automatic analysis procedure able to correctly acquire all the important morphological parameters of the hepatic spheroids under static and after stimulation conditions. Within the datasets, several issues can occur related to the non-uniform illumination background and inherent limitation in counting the exact object number when two or more are adjacent. Some of the images include patterns with intensity comparable to the spheroid intensity, such as extended grooves, and they are filtered out based on their eccentricity values. Traditional methods such as Otsu threshold does not segment the spheroid images truthfully due to their energy minimization based approach. To circumvent this limitation initially background removal is applied as a preprocessing step. Filters applied for this depend on the relative size of the spheroids in order not to diminish the image quality. Therefore, we propose a guided automatic threshold value that can discriminate between the background and the spheroids more accurately by finding the critical peak on the pixel intensity histogram. Pixel intensity histograms are composed of three modes and the local minimum after the peak at the lowest values is the threshold value. After applying the new guided thresholding technique, watershed algorithm is used in order to determine the separating nodes between objects that are contiguous to each-other. These two techniques are compared with Gabor filters based methods that are shape based filters. Employing the three methods spheroid parameters including the number, area and the perimeter were determined and their performance and robustness are discussed.

Keywords

Spheroid culture Segmentation Guided thresholding 

Notes

Acknowledgments

This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 760921 (PANBioRA).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Engineering DepartmentEpoka UniversityTiranaAlbania
  2. 2.Department of Information TechnologyAleksander Moisiu UniversityDurresAlbania
  3. 3.School of Life Sciences, Faculty of Medicine and Health SciencesUniversity of NottinghamNottinghamUK
  4. 4.INSERM, UMR 1121StrasbourgFrance
  5. 5.Spartha MedicalStrasbourgFrance

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