  This study aimed to evaluate the performance of a novel unsupervised deep
learning-based framework for automated infections lesion segmentation from CT
images of Covid patients. In the first step, two residual networks were
independently trained to identify the lung tissue for normal and Covid patients
in a supervised manner. These two models, referred to as DL-Covid and DL-Norm
for Covid-19 and normal patients, respectively, generate the voxel-wise
probability maps for lung tissue identification. To detect Covid lesions, the
CT image of the Covid patient is processed by the DL-Covid and DL-Norm models
to obtain two lung probability maps. Since the DL-Norm model is not familiar
with Covid infections within the lung, this model would assign lower
probabilities to the lesions than the DL-Covid. Hence, the probability maps of
the Covid infections could be generated through the subtraction of the two lung
probability maps obtained from the DL-Covid and DL-Norm models. Manual lesion
segmentation of 50 Covid-19 CT images was used to assess the accuracy of the
unsupervised lesion segmentation approach. The Dice coefficients of 0.985 and
0.978 were achieved for the lung segmentation of normal and Covid patients in
the external validation dataset, respectively. Quantitative results of
infection segmentation by the proposed unsupervised method showed the Dice
coefficient and Jaccard index of 0.67 and 0.60, respectively. Quantitative
evaluation of the proposed unsupervised approach for Covid-19 infectious lesion
segmentation showed relatively satisfactory results. Since this framework does
not require any annotated dataset, it could be used to generate very large
training samples for the supervised machine learning algorithms dedicated to
noisy and/or weakly annotated datasets.
