Signal Detection in Three-Dimensional Confocal Microscopy Images Through Deep Learning

As routine pathology moves into the digital age; the spread of high-efficiency and high-resolution tissue scanners opens up the possibility of routine analysis of three-dimensional samples containing fluorescent genetic signals. One of these cornerstones is confocal microscopy, with the help of which cell nuclei and their signals can be imaged in three dimensions. This article presents a novel deep learning-based algorithm for detecting signals within three-dimensional confocal microscopy images. By leveraging the power of convolutional neural networks, our approach significantly improves the accuracy and efficiency of signal detection compared to traditional image processing methods, especially in the case of thick sections. We demonstrate the algorithmic effectiveness through validation on various samples, highlighting its potential to advance research in biology and medicine.