Machine learning representation of loss of eye regularity in a Drosophila Neurodegenerative model


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Díez-Hernando, Sergio and Ganfornina, María D. and Vargas Lozano, Esteban and Sánchez, Diego (2020) Machine learning representation of loss of eye regularity in a Drosophila Neurodegenerative model. Frontiers in Neuroscience, 14 (516). pp. 1-12. ISSN 1662-4548, ESSN: 1662-453X

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The fruit fly compound eye is a premier experimental system for modeling human neurodegenerative diseases. The disruption of the retinal geometry has been historically assessed using time-consuming and poorly reliable techniques such as histology or pseudopupil manual counting. Recent semiautomated quantification approaches rely either on manual region-of-interest delimitation or engineered features to estimate the extent of degeneration. This work presents a fully automated classification pipeline of bright-field images based on orientated gradient descriptors and machine learning techniques. An initial region-of-interest extraction is performed, applying morphological kernels and Euclidean distance-to-centroid thresholding. Image classification algorithms are trained on these regions (support vector machine, decision trees, random forest, and convolutional neural network), and their performance is evaluated on independent, unseen datasets. The combinations of oriented gradient + gaussian kernel Support Vector Machine [0.97 accuracy and 0.98 area under the curve (AUC)] and fine-tuned pre-trained convolutional neural network (0.98 accuracy and 0.99 AUC) yielded the best results overall. The proposed method provides a robust quantification framework that can be generalized to address the loss of regularity in biological patterns similar to the Drosophila eye surface and speeds up the processing of large sample batches.

Item Type:Article
Uncontrolled Keywords:Drosophila melanogaster; Neurodegeneration; Rough eye; Phenotype; Spinocerebellar ataxia; Machine learning; Classification; Deep learning
Subjects:Medical sciences > Biology > Molecular biology
Medical sciences > Biology > Biomathematics
Medical sciences > Biology > Neurosciences
ID Code:62533
Deposited On:14 Oct 2020 07:46
Last Modified:14 Oct 2020 07:46

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