Lung ultrasound (LUS) is an increasingly popular diagnostic imaging modality for continuous and periodic monitoring of lung infection, given its advantages of non-invasiveness, non-ionizing nature, portability and easy disinfection. The major landmarks assessed by clinicians for triaging using LUS are pleura, A and B lines. There have been many efforts for the automatic detection of these landmarks. However, restricting to a few pre-defined landmarks may not reveal the actual imaging biomarkers particularly in case of new pathologies like COVID-19. Rather, the identification of key landmarks should be driven by data given the availability of a plethora of neural network algorithms. This work is a first of its kind attempt towards unsupervised detection of the key LUS landmarks in LUS videos of COVID-19 subjects during various stages of infection. We adapted the relatively newer approach of transporter neural networks to automatically mark and track pleura, A and B lines based on their periodic motion and relatively stable appearance in the videos. Initial results on unsupervised pleura detection show an accuracy of 91.8% employing 1081 LUS video frames.
@article{arxiv.2106.06987,
title = {Learning the Imaging Landmarks: Unsupervised Key point Detection in Lung Ultrasound Videos},
author = {Arpan Tripathi and Mahesh Raveendranatha Panicker and Abhilash R Hareendranathan and Yale Tung Chen and Jacob L Jaremko and Kiran Vishnu Narayan and Kesavadas C},
journal= {arXiv preprint arXiv:2106.06987},
year = {2021}
}