A new software tool that reveals the severity of lung infections in COVID-19 patients has been developed by researchers from the Departments of Computational and Data Science (CDS) and Instrumentation and Applied Physics at the Indian Institute of Science (IISc), in collaboration with colleagues from the Oslo University Hospital and the University of Agder in Norway.
The software tool, which is freely available to the public, has been described in a recent study published in the journal IEEE Transactions on Neural Networks and Learning Systems.
While it is known that COVID-19 can cause severe damage to the respiratory systems, especially the lung tissues, and methods such as X-ray or CT scans can prove helpful in determining how bad the infection is, an IISc release said the software tool, called AnamNet, can ‘read’ the chest CT scans of COVID-19 patients, and, using a special kind of neural network, estimate how much damage has been caused in the lungs by searching for specific abnormal features. Such a tool can provide automated assistance to doctors and therefore help in faster diagnosis and better management of COVID-19, it says.
“AnamNet employs deep learning and other image processing techniques, which have now become integral to biomedical research and applications. The software can identify infected areas in a chest CT scan with a high degree of accuracy. The researchers trained AnamNet to look for abnormalities and classify areas of the lung scan as either infected or not infected ‒ this is called ‘segmentation’. The tool can judge the severity of the disease by comparing the extent of infected area with healthy area,” the release explained.
The release quoted Naveen Paluru, first author and PhD student in the lab of Phaneendra Yalavarthy, Associate Professor at CDS, explaining that the tool extracts features from the chest CT images and projects them onto a non-linear space [a mathematical representation], and then recreates the segmented image from this representation. “This is called anamorphic image processing,” he said.
Another significant advantage of AnamNet is that the software is lightweight with a small memory footprint. This has enabled the team to develop an app called CovSeg that can be run on a mobile phone and hence potentially be used by healthcare professionals, the release further added. The researchers have cited this as a feature missing from currently available state-of-the-art technologies, which require specialised hardware.
The researchers are now looking at diversifying the tool to other common lung diseases such ase pneumonia, fibrosis and even lung cancer in the near future.
A sample demo of the tool can be found in the link: https://github.com/NaveenPaluru/Segmentation-COVID-19
