Faster R-CNN approach for detection and quantification of DNA damage in comet assay images

Abstract

Background and objective: DNA damage analysis can provide valuable information in several areas ranging from the diagnosis/treatment of a disease to the monitoring of the effects of genetic and environmental influences. The evaluation of the damage is determined by comet scoring, which can be performed by a skilled operator with a manual procedure. However, this approach becomes very time-consuming and the operator dependency results in the subjectivity of the damage quantification and thus in a high inter/intra-operator variability.

Methods: In this paper, we aim to overcome this issue by introducing a Deep Learning methodology based on Faster R-CNN to completely automatize the overall approach while discovering unseen discriminative patterns in comets.

Results: The experimental results performed on two real use-case datasets reveal the higher performance (up to mean absolute precision of 0.74) of the proposed methodology against other state-of-the-art approaches. Additionally, the validation procedure performed by expert biologists highlights how the proposed approach is able to unveil true comets, often unseen from the human eye and standard computer vision methodology.

Conclusions: This work contributes to the biomedical informatics field by the introduction of a novel approach based on established object detection Deep Learning technique for evaluating the DNA damage. The main contribution is the application of Faster R-CNN for the detection and quantification of DNA damage in comet assay images, by fully automatizing the detection/classification DNA damage task. The experimental results extracted in two real use-case datasets demonstrated (i) the higher robustness of the proposed methodology against other state-of-the-art Deep Learning competitors, (ii) the speeding up of the comet analysis procedure and (iii) the minimization of the intra/inter-operator variability.

Keywords: Comet assay; DNA damage classification; Deep learning; Faster R-CNN; Pattern recognition.