Cut-Puzzle mix: Scribble Guided Medical Image Segmentation without Segmentation Masks

Majority of contemporary fully supervised segmentation algorithms excel in quantifying human anatomy, significantly advancing the field. However, the reliance of numerous deep neural networks on extensive datasets with full pixel-wise annotations poses challenges. The creation of such annotated training data is both laborious and financially demanding. In response to these challenges, there has been a notable shift in focus towards leveraging limited data, specifically in the form of scribble annotations. This study explores training strategies aimed at learning the parameters of a pixel-wise segmentation network solely from scribble annotations, employing cut-mix and puzzle mix strategies. To further enhance supervision regularization, consistency losses are incorporated with cross entropy, penalizing inconsistent segmentation and leading to a noteworthy improvement in segmentation performance. Evaluation of these techniques is conducted on publicly available cardiac (ACDC) and MSCMR segmentation datasets. Our proposed method demonstrates impressive performance, surpassing the results achieved by state-of-the-art methods.