Mask uncertainty regularization to improve machine learning-based medical image segmentation

Segmentation of the different structures on CT and MRI scans is still challenging problem which requires very accurate and confident ground truth (GT) segmentation and strong automated solutions. This work present an approach to naturally adjust the training process by smoothing the borders of the segmentation mask in the band of several pixels. The proposed method can be considered as either regularization or data pre-processing step to compensate uncertainty of the GT. This method can be used for any organs segmentation problem both binary and multiclass. As one of the applications we report results in terms of Dice, Precision and Recall scores of the numerical experiments for the binary segmentation of the spleen on abdominal CT scans. Obtained results demonstrate stable and continuous improvement from very strong baseline in target Dice metric up to 3.8% for one fold and 1% for the mean averaged 5-fold ensemble, achieving Dice of 0.9486 and Recall 0.9535.