Investigation of the Organisation of the Extracellular Matrix Using Fibre Tracing
| dc.contributor.author | Arafat, Youssef | en_US |
| dc.contributor.author | Cuesta-Apausa, Cristina | en_US |
| dc.contributor.author | Castellano, Esther | en_US |
| dc.contributor.author | Reyes-Aldasoro, Constantino Carlos | en_US |
| dc.contributor.editor | Hunter, David | en_US |
| dc.contributor.editor | Slingsby, Aidan | en_US |
| dc.date.accessioned | 2024-09-09T05:45:39Z | |
| dc.date.available | 2024-09-09T05:45:39Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Throughout history, the observation of medical and biological samples has been of high importance and has led to many discoveries. When this process relies on human observation, it can be time-consuming, especially with the advent of technological advancements that generate more and more images at faster rates. Additionally, some features of the samples can be undetectable by the naked eye, but with the aid of visual computing techniques, these hidden details can be revealed. The morphological characteristics of the extracellular matrix play a vital role in cancer and other health conditions. Visual observations of the ECM can provide valuable insights; however, the task may be tedious and sometimes it is hard to quantify the differences between samples. In this work, a tracing algorithm is proposed. Furthermore, morphological characteristics of the extracellular matrix can be extracted with the algorithm to quantify and compare different biological populations. Experiments revealed that the removal of interactions in fibroblasts affected their ability to form a healthy extracellular matrix as compared with a wild type population. Here, an investigation of the morphological differences between the ECM of two populations was conducted. Five images of mutant and five images of wild type cells growing in culture were compared. A deconvolutional convolutional neural network was used as a pre-processing filtering method to remove noise from the images. The images are then traced by the proposed algorithm, Trace Ridges, to extract morphological features and visually present the edges and gaps extracted. Trace Ridges combines methods of Edge detection, watershed, and morphological characteristics to delineate fibre-like structures. Two morphological characteristics provided statistical differences between the populations: number of fibres (p−value = 0.00091) and relative area of gaps between the fibres (p−value = 0.014). The number of fibres detected in wild type was higher than mutant while the relative gaps area size of mutant was higher than that of WT. Trace Ridges was able to successfully delineate the ECM fibres of mutant and wild type cells and extract morphological features to show the difference between the populations. | en_US |
| dc.description.sectionheaders | Machine Learning and LLM-enabled Visual Analytics | |
| dc.description.seriesinformation | Computer Graphics and Visual Computing (CGVC) | |
| dc.identifier.doi | 10.2312/cgvc.20241238 | |
| dc.identifier.isbn | 978-3-03868-249-3 | |
| dc.identifier.pages | 8 pages | |
| dc.identifier.uri | https://doi.org/10.2312/cgvc.20241238 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/cgvc20241238 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Computing methodologies → Object detection | |
| dc.subject | Computing methodologies → Object detection | |
| dc.title | Investigation of the Organisation of the Extracellular Matrix Using Fibre Tracing | en_US |
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