  Consistent segmentation of COVID-19 patient's CT scans across multiple time
points is essential to assess disease progression and response to therapy
accurately. Existing automatic and interactive segmentation models for medical
images only use data from a single time point (static). However, valuable
segmentation information from previous time points is often not used to aid the
segmentation of a patient's follow-up scans. Also, fully automatic segmentation
techniques frequently produce results that would need further editing for
clinical use. In this work, we propose a new single network model for
interactive segmentation that fully utilizes all available past information to
refine the segmentation of follow-up scans. In the first segmentation round,
our model takes 3D volumes of medical images from two-time points (target and
reference) as concatenated slices with the additional reference time point
segmentation as a guide to segment the target scan. In subsequent segmentation
refinement rounds, user feedback in the form of scribbles that correct the
segmentation and the target's previous segmentation results are additionally
fed into the model. This ensures that the segmentation information from
previous refinement rounds is retained. Experimental results on our in-house
multiclass longitudinal COVID-19 dataset show that the proposed model
outperforms its static version and can assist in localizing COVID-19 infections
in patient's follow-up scans.
