RT Journal Article
T1 Machine learning representation of loss of eye regularity in a Drosophila Neurodegenerative model
A1 Díez-Hernando, Sergio
A1 Ganfornina, María D.
A1 Vargas Lozano, Esteban
A1 Sánchez, Diego
AB 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.
PB Frontiers Media
SN 1662-4548, ESSN: 1662-453X
YR 2020
FD 2020-06-04
LK https://hdl.handle.net/20.500.14352/6640
UL https://hdl.handle.net/20.500.14352/6640
LA eng
NO Ministerio de Ciencia e Innovación (MICINN)/Fondo Europeo de Desarrollo Regional (FEDER)
DS Docta Complutense
RD 7 abr 2025