Abstract:
The last 2 years have been adversely affected by the COVID-19 pandemic. Doctors
usually detect Covid from CT slices from features such as ground glass, consolidation
and pleural effusion. These features usually have complex contours, irregular
shapes and rough boundaries. With increasing number of cases the workload on
the radiologists have increased by leaps and bounds to analyze the lung CT scans
for tracking the disease progression in the patient. Moreover manual analysis of
the CT scans is also prone to human error. So automated segmentation of infected
lung CT slices can help the doctors to diagnose the disease faster. With the advent
of deep learning, various approaches have been built to tackle this problem of
automated biomedical image segmentation. One such architecture is the U-Net by
Ronnenberger et al. [14]. Various other approaches have been proposed which are
all variations of the U-Net to achieve better segmentation performance. However,
the U-Net and its variations suffer from high model complexity, due to which they
easily overfit on limited labelled dataset which is a serious issue in medical image
domain. To cater this problem of data scarcity, research in “few shot segmentation”
has gained significant importance in the recent years. In this work, we have developed
a deep neural network model called Few Shot Conditioner Segmenter Covid
(FSCS-cov), an architecture to tackle the problem of segmenting different COVID-
19 lesions from limited number of COVID-19 infected lung CT slices using few -
shot learning paradigm.
