ArXiv Preprint
Interactive segmentation reduces the annotation time of medical images and
allows annotators to iteratively refine labels with corrective interactions,
such as clicks. While existing interactive models transform clicks into user
guidance signals, which are combined with images to form (image, guidance)
pairs, the question of how to best represent the guidance has not been fully
explored. To address this, we conduct a comparative study of existing guidance
signals by training interactive models with different signals and parameter
settings to identify crucial parameters for the model's design. Based on our
findings, we design a guidance signal that retains the benefits of other
signals while addressing their limitations. We propose an adaptive Gaussian
heatmaps guidance signal that utilizes the geodesic distance transform to
dynamically adapt the radius of each heatmap when encoding clicks. We conduct
our study on the MSD Spleen and the AutoPET datasets to explore the
segmentation of both anatomy (spleen) and pathology (tumor lesions). Our
results show that choosing the guidance signal is crucial for interactive
segmentation as we improve the performance by 14% Dice with our adaptive
heatmaps on the challenging AutoPET dataset when compared to non-interactive
models. This brings interactive models one step closer to deployment on
clinical workflows. We will make our code publically available.
Zdravko Marinov, Rainer Stiefelhagen, Jens Kleesiek
2023-03-13
