Machine Learning Representation of Loss of Eye Regularity in a Drosophila Neurodegenerative Model - Diez-Hermano, Sergio; Ganfornina, Maria D.; Vegas-Lozano, Esteban; Sanchez, Diego (Frontiers in Neuroscience, 2020)
Fruit fly compound eye is a premier experimental system for modelling human neurodegenerative diseases. 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 degeneration extent. 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, 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 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 loss of regularity in biological patterns similar to the Drosophila eye surface and speeds up the processing of large sample batches.