  One of the core challenges facing the medical image computing community is
fast and efficient data sample labeling. Obtaining fine-grained labels for
segmentation is particularly demanding since it is expensive, time-consuming,
and requires sophisticated tools. On the contrary, applying bounding boxes is
fast and takes significantly less time than fine-grained labeling, but does not
produce detailed results. In response, we propose a novel framework for
weakly-supervised tasks with the rapid and robust transformation of bounding
boxes into segmentation masks without training any machine learning model,
coined BoxShrink. The proposed framework comes in two variants -
rapid-BoxShrink for fast label transformations, and robust-BoxShrink for more
precise label transformations. An average of four percent improvement in IoU is
found across several models when being trained using BoxShrink in a
weakly-supervised setting, compared to using only bounding box annotations as
inputs on a colonoscopy image data set. We open-sourced the code for the
proposed framework and published it online.
