Hairy Ground Truth Enhancement for Semantic Segmentation

Sophie Fischer, Irina Voiculescu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 2404-2412

Semantic segmentation is a key task within applications of machine learning for medical imaging requiring large amounts of medical scans annotated by clinicians. The high cost of data annotation means that models need to make the most of all available ground truth masks; yet many models consider two false positive (or false negative) pixel predictions as 'equally wrong' regardless of the individual pixels' relative position to the ground truth mask. These methods also have no sense of whether a pixel is solitary or belongs to a contiguous group. We propose the Hairy transform a novel method to enhance ground truths using 3D 'hairs' to represent each pixel's position relative to objects in the ground truth. We illustrate its effectiveness using a mainstream model and loss function on a commonly used cardiac MRI dataset as well as a set of synthetic data constructed to highlight the effect of the method during training. The overall improvement in segmentation results comes at the small cost of a one off pre-processing step and can easily be integrated into any standard machine learning model. Rather than looking to make minute improvements for mostly correct 'standard' masks we instead show how this method helps improve robustness against catastrophic failures for edge cases.