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Unsupervised Cell Segmentation and Labelling in Neural Tissue Images

Iglesias-Rey, S; Antunes-Santos, F; Hagemann, C; Gómez-Cabrero, D; Bustince, H; Patani, R; Serio, A; ... Lopez-Molina, C; + view all (2021) Unsupervised Cell Segmentation and Labelling in Neural Tissue Images. Applied Sciences , 11 (9) p. 3733. 10.3390/app11093733. Green open access

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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.

Type: Article
Title: Unsupervised Cell Segmentation and Labelling in Neural Tissue Images
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/app11093733
Publisher version: https://doi.org/10.3390/app11093733
Language: English
Additional information: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Keywords: neurodegenerative diseases; medical imaging; object segmentation; binary image; image processing; amyotrophic lateral sclerosis
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Department of Neuromuscular Diseases
URI: https://discovery.ucl.ac.uk/id/eprint/10127159
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