Proton therapy for cancer treatment is a rapidly growing field and increasing evidence suggests it induces more complex DNA damage than photon therapy. Accurate comparison between the two treatments requires quantification of the damage caused, one method being the comet assay. The program outlined here is based on neural network architecture and aims to speed up analysis of comet assay images and provide accurate, quantified assessment of the DNA damage levels apparent in them.
The comet assay is an established technique in which DNA fragments are spread out under the influence of an electric field, producing a comet-like object. The elongation and intensity of the comet tail (consisting of DNA fragments) indicate the level of damage incurred. Many methods to measure this damage exist, using a variety of algorithms. These can be time consuming, so often only a small fraction of the comets available in an image are analysed. The automatic analysis presented here aims to improve this.
Object detection and localisation, implemented by a Mask-RCNN neural network, are used to perform instance segmentation of the comets. The identified comet instances are then saved as masks, which when overlaid onto the original image, provide pixel coordinates of the identified comets. A minimum accuracy of 90% has been achieved by the model in identifying comets in an image. The model has been trained via transfer learning from Microsoft’s extensive COCO model, which is based on over 200,000 labelled images. This has significantly reduced both training time and also the number of images required for training (less than 70 images have been used here).
To supplement the training and testing of the network a Monte Carlo model is being developed in order to create simulated comet assay images.