Unsupervised Cell Segmentation and Labelling in Neural Tissue Images
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 acros...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/9/3733 |
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| author | Sara Iglesias-Rey Felipe Antunes-Santos Cathleen Hagemann David Gómez-Cabrero Humberto Bustince Rickie Patani Andrea Serio Bernard De Baets Carlos Lopez-Molina |
| author_facet | Sara Iglesias-Rey Felipe Antunes-Santos Cathleen Hagemann David Gómez-Cabrero Humberto Bustince Rickie Patani Andrea Serio Bernard De Baets Carlos Lopez-Molina |
| author_sort | Sara Iglesias-Rey |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:07:34Z |
| format | Article |
| id | doaj.art-60d92d564ba141bea755d0268d76bc5a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T12:07:34Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-60d92d564ba141bea755d0268d76bc5a2023-11-21T16:25:27ZengMDPI AGApplied Sciences2076-34172021-04-01119373310.3390/app11093733Unsupervised Cell Segmentation and Labelling in Neural Tissue ImagesSara Iglesias-Rey0Felipe Antunes-Santos1Cathleen Hagemann2David Gómez-Cabrero3Humberto Bustince4Rickie Patani5Andrea Serio6Bernard De Baets7Carlos Lopez-Molina8Department of Estadistica, Informatica y Matematicas, Universidad Publica de Navarra, 31006 Pamplona, SpainDepartment of Estadistica, Informatica y Matematicas, Universidad Publica de Navarra, 31006 Pamplona, SpainThe Francis Crick Institute, 1 Midland Road, London NW1 1AT, UKNavarraBiomed, Complejo Hospitalario de Navarra, 31008 Pamplona, SpainDepartment of Estadistica, Informatica y Matematicas, Universidad Publica de Navarra, 31006 Pamplona, SpainThe Francis Crick Institute, 1 Midland Road, London NW1 1AT, UKThe Francis Crick Institute, 1 Midland Road, London NW1 1AT, UKKERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000 Ghent, BelgiumDepartment of Estadistica, Informatica y Matematicas, Universidad Publica de Navarra, 31006 Pamplona, SpainNeurodegenerative 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.https://www.mdpi.com/2076-3417/11/9/3733neurodegenerative diseasesmedical imagingobject segmentationbinary imageimage processingamyotrophic lateral sclerosis |
| spellingShingle | Sara Iglesias-Rey Felipe Antunes-Santos Cathleen Hagemann David Gómez-Cabrero Humberto Bustince Rickie Patani Andrea Serio Bernard De Baets Carlos Lopez-Molina Unsupervised Cell Segmentation and Labelling in Neural Tissue Images Applied Sciences neurodegenerative diseases medical imaging object segmentation binary image image processing amyotrophic lateral sclerosis |
| title | Unsupervised Cell Segmentation and Labelling in Neural Tissue Images |
| title_full | Unsupervised Cell Segmentation and Labelling in Neural Tissue Images |
| title_fullStr | Unsupervised Cell Segmentation and Labelling in Neural Tissue Images |
| title_full_unstemmed | Unsupervised Cell Segmentation and Labelling in Neural Tissue Images |
| title_short | Unsupervised Cell Segmentation and Labelling in Neural Tissue Images |
| title_sort | unsupervised cell segmentation and labelling in neural tissue images |
| topic | neurodegenerative diseases medical imaging object segmentation binary image image processing amyotrophic lateral sclerosis |
| url | https://www.mdpi.com/2076-3417/11/9/3733 |
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