Applied Sciences (Apr 2021)

Unsupervised Cell Segmentation and Labelling in Neural Tissue Images

  • Sara Iglesias-Rey,
  • Felipe Antunes-Santos,
  • Cathleen Hagemann,
  • David Gómez-Cabrero,
  • Humberto Bustince,
  • Rickie Patani,
  • Andrea Serio,
  • Bernard De Baets,
  • Carlos Lopez-Molina

DOI
https://doi.org/10.3390/app11093733
Journal volume & issue
Vol. 11, no. 9
p. 3733

Abstract

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Neurodegenerative diseases are a group of largely incurable disorders characterised by the progressive loss of neurons and for which often the molecular mechanisms are poorly understood. To bridge this gap, researchers employ a range of techniques. A very prominent and useful technique adopted across many different fields is imaging and the analysis of histopathological and fluorescent label tissue samples. Although image acquisition has been efficiently automated recently, automated analysis still presents a bottleneck. Although various methods have been developed to automate this task, they tend to make use of single-purpose machine learning models that require extensive training, imposing a significant workload on the experts and introducing variability in the analysis. Moreover, these methods are impractical to audit and adapt, as their internal parameters are difficult to interpret and change. Here, we present a novel unsupervised automated schema for object segmentation of images, exemplified on a dataset of tissue images. Our schema does not require training data, can be fully audited and is based on a series of understandable biological decisions. In order to evaluate and validate our schema, we compared it with a state-of-the-art automated segmentation method for post-mortem tissues of ALS patients.

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