  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.
