Next Article in Journal
Which Is the Motion State of a Droplet on an Inclined Hydrophilic Rough Surface in Gravity: Pinned or Sliding?
Next Article in Special Issue
Diagnosis of Obstructive Sleep Apnea from ECG Signals Using Machine Learning and Deep Learning Classifiers
Previous Article in Journal
The Role of Metal Ions in the Electron Transport through Azurin-Based Junctions
Previous Article in Special Issue
Machine Learning in Chronic Pain Research: A Scoping Review
Article

Unsupervised Cell Segmentation and Labelling in Neural Tissue Images

1
Department of Estadistica, Informatica y Matematicas, Universidad Publica de Navarra, 31006 Pamplona, Spain
2
NavarraBiomed, Complejo Hospitalario de Navarra, 31008 Pamplona, Spain
3
KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000 Ghent, Belgium
4
The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
5
Centre for Craniofacial & Regenerative Biology, King’s College London, London WC2R 2LS, UK
6
Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
7
Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Yahan Hu
Appl. Sci. 2021, 11(9), 3733; https://doi.org/10.3390/app11093733
Received: 14 March 2021 / Revised: 12 April 2021 / Accepted: 18 April 2021 / Published: 21 April 2021
(This article belongs to the Special Issue Medical Artificial Intelligence)
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. View Full-Text
Keywords: neurodegenerative diseases; medical imaging; object segmentation; binary image; image processing; amyotrophic lateral sclerosis neurodegenerative diseases; medical imaging; object segmentation; binary image; image processing; amyotrophic lateral sclerosis
Show Figures

Figure 1

MDPI and ACS Style

Iglesias-Rey, S.; Antunes-Santos, F.; Hagemann, C.; Gómez-Cabrero, D.; Bustince, H.; Patani, R.; Serio, A.; De Baets, B.; Lopez-Molina, C. Unsupervised Cell Segmentation and Labelling in Neural Tissue Images. Appl. Sci. 2021, 11, 3733. https://doi.org/10.3390/app11093733

AMA Style

Iglesias-Rey S, Antunes-Santos F, Hagemann C, Gómez-Cabrero D, Bustince H, Patani R, Serio A, De Baets B, Lopez-Molina C. Unsupervised Cell Segmentation and Labelling in Neural Tissue Images. Applied Sciences. 2021; 11(9):3733. https://doi.org/10.3390/app11093733

Chicago/Turabian Style

Iglesias-Rey, Sara, Felipe Antunes-Santos, Cathleen Hagemann, David Gómez-Cabrero, Humberto Bustince, Rickie Patani, Andrea Serio, Bernard De Baets, and Carlos Lopez-Molina. 2021. "Unsupervised Cell Segmentation and Labelling in Neural Tissue Images" Applied Sciences 11, no. 9: 3733. https://doi.org/10.3390/app11093733

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop