Automatic detection and counting of retina cell nuclei using deep learning
This work addresses the need for automated quantification of retinal cells in diseases like age-related macular degeneration, representing an incremental improvement with a specific application in medical imaging.
The researchers tackled the problem of automatically detecting and counting retinal cell nuclei in transmission electron microscopy images to aid in eye disease analysis, achieving high accuracy in detection, categorization, and counting using a deep learning-based Mask R-CNN model.
The ability to automatically detect, classify, calculate the size, number, and grade of retinal cells and other biological objects is critically important in eye disease like age-related macular degeneration (AMD). In this paper, we developed an automated tool based on deep learning technique and Mask R-CNN model to analyze large datasets of transmission electron microscopy (TEM) images and quantify retinal cells with high speed and precision. We considered three categories for outer nuclear layer (ONL) cells: live, intermediate, and pyknotic. We trained the model using a dataset of 24 samples. We then optimized the hyper-parameters using another set of 6 samples. The results of this research, after applying to the test datasets, demonstrated that our method is highly accurate for automatically detecting, categorizing, and counting cell nuclei in the ONL of the retina. Performance of our model was tested using general metrics: general mean average precision (mAP) for detection; and precision, recall, F1-score, and accuracy for categorizing and counting.