  Current state-of-the-art supervised deep learning-based segmentation
approaches have demonstrated superior performance in medical image segmentation
tasks. However, such supervised approaches require fully annotated pixel-level
ground-truth labels, which are labor-intensive and time-consuming to acquire.
Recently, Scribble2Label (S2L) demonstrated that using only a handful of
scribbles with self-supervised learning can generate accurate segmentation
results without full annotation. However, owing to the relatively small size of
scribbles, the model is prone to overfit and the results may be biased to the
selection of scribbles. In this work, we address this issue by employing a
novel multiscale contrastive regularization term for S2L. The main idea is to
extract features from intermediate layers of the neural network for contrastive
loss so that structures at various scales can be effectively separated. To
verify the efficacy of our method, we conducted ablation studies on well-known
datasets, such as Data Science Bowl 2018 and MoNuSeg. The results show that the
proposed multiscale contrastive loss is effective in improving the performance
of S2L, which is comparable to that of the supervised learning segmentation
method.
