Resumen:
In this work, a Deep Learning-based machine vision model was devel-
oped for the detection, segmentation and counting of Neural Progenitor Cell nuclei
from fluorescence microscopy images. The cells were obtained from adult mice
and cultivated in vitro, with cellular nuclei labeled using DAPI dye. Convolu-
tional neural networks for instance segmentation, specifically the Mask R-CNN
model with ResNet-50 and ResNet-101 backbones, were trained to recognize the
nuclei, and their results were evaluated. Nuclei labeling was implemented semi-
automatically, applying a Superpixel technique and then refining the segmentations
from a manual process, also using a pre-trained model, which allowed to assem-
ble a dataset of 66 images with 6392 labels in total. The results obtained with the
Resnet-50 backbone show that there is an effectiveness of 98.6% for between the
specialist count and model-predicted count, in addition to having an mAP50 of
98.0%. This approach has the potential to significantly reduce the time and effort
required to analyze large image sets, which is especially useful in studies that
require repetitive and detailed cellular analysis.
