Detecting cells in intravital video microscopy using a deep convolutional neural network

• Published in: Computers in biology and medicine Vol. 129; p. 104133
• Main Authors: Gregório da Silva, Bruno C., Tam, Roger, Ferrari, Ricardo J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.02.2021; Elsevier Limited
• Subjects: Airy patterns; Artificial neural networks; Cell detection; Centroids; Color vision; Convolutional neural network; Data augmentation; Datasets; Deep learning; Ground truth; Image processing; Inflammation; Learning algorithms; Leukocyte recruitment; Leukocytes; Machine learning; Methods; Microscopy; Neural networks; Software; Source code; Transfer learning; Deep learning; Leukocyte recruitment; Convolutional neural network; Cell detection
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2020.104133

The analysis of leukocyte recruitment in intravital video microscopy (IVM) is essential to the understanding of inflammatory processes. However, because IVM images often present a large variety of visual characteristics, it is hard for an expert human or even conventional machine learning techniques to detect and count the massive amount of cells and extract statistical measures precisely. Convolutional neural networks are a promising approach to overcome this problem, but due to the difficulty of labeling cells, large data sets with ground truth are rare. The present work explores an adaptation of the RetinaNet model with a suite of augmentation techniques and transfer learning for detecting leukocytes in IVM data. The augmentation techniques include simulating the Airy pattern and motion artifacts present in microscopy imaging, followed by traditional photometric, geometric and smooth elastic transformations to reproduce color and shape changes in cells. In addition, we analyzed the use of different network backbones, feature pyramid levels, and image input scales. We have found that even with limited data, our strategy not only enables training without overfitting but also boosts generalization performance. Among several experiments, the model reached a value of 94.84 for the average precision (AP) metric as our best outcome when using data from different image modalities. We also compared our results with conventional image processing techniques and open-source tools. The results showed an outstanding precision of the method compared with other approaches, presenting low error rates for cell counting and centroid distances. Code is available at: https://github.com/brunoggregorio/retinanet-cell-detection. •Intravital video microscopy dataset with several manually annotated cells.•Adaptation of RetinaNet model for leukocyte detection in intravital video microscopy.•Suite of data augmentation techniques to simulate microscopy image variations.•Comparison with benchmark methods in object detection.