  Automatic segmentation of lung lesions associated with COVID-19 in CT images
requires large amount of annotated volumes. Annotations mandate expert
knowledge and are time-intensive to obtain through fully manual segmentation
methods. Additionally, lung lesions have large inter-patient variations, with
some pathologies having similar visual appearance as healthy lung tissues. This
poses a challenge when applying existing semi-automatic interactive
segmentation techniques for data labelling. To address these challenges, we
propose an efficient convolutional neural networks (CNNs) that can be learned
online while the annotator provides scribble-based interaction. To accelerate
learning from only the samples labelled through user-interactions, a
patch-based approach is used for training the network. Moreover, we use
weighted cross-entropy loss to address the class imbalance that may result from
user-interactions. During online inference, the learned network is applied to
the whole input volume using a fully convolutional approach. We compare our
proposed method with state-of-the-art using synthetic scribbles and show that
it outperforms existing methods on the task of annotating lung lesions
associated with COVID-19, achieving 16% higher Dice score while reducing
execution time by 3$\times$ and requiring 9000 lesser scribbles-based labelled
voxels. Due to the online learning aspect, our approach adapts quickly to user
input, resulting in high quality segmentation labels. Source code for ECONet is
available at: https://github.com/masadcv/ECONet-MONAILabel.
