Unsupervised Cell Segmentation and Labelling in Neural Tissue Images

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

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish (US)
Pages (from-to)3733
JournalAPPLIED SCIENCES-BASEL
Volume11
Issue number9
DOIs
StatePublished - Apr 21 2021
Externally publishedYes

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