Automatic semantic segmentation for prediction of tuberculosis using lens-free microscopy images
This work addresses the problem of facilitating and automating TB diagnosis for public health systems, particularly in Peru, using more accessible lens-free microscopy, though it appears incremental as it applies an existing method to a new dataset.
The researchers tackled automating tuberculosis diagnosis by using a U-Net network for semantic segmentation of TB cords in lens-free microscopy images, with initial results showing promising evidence for this approach.
Tuberculosis (TB), caused by a germ called Mycobacterium tuberculosis, is one of the most serious public health problems in Peru and the world. The development of this project seeks to facilitate and automate the diagnosis of tuberculosis by the MODS method and using lens-free microscopy, due they are easier to calibrate and easier to use (by untrained personnel) in comparison with lens microscopy. Thus, we employ a U-Net network in our collected dataset to perform the automatic segmentation of the TB cords in order to predict tuberculosis. Our initial results show promising evidence for automatic segmentation of TB cords.