Using Temporal Similarity in Contrastive Learning for Multi-class Kidney Ultrasound Segmentation

Creating ground truth segmentations for medical imaging is labour and time intensive. While promising, contemporary contrastive learning techniques commonly overlook the ultrasound domain. We investigate the potential benefits of using ultrasound's real-time trait through different contrastive learning sampling strategies in multi-class semantic segmentation. First, we perform a head-to-head label efficiency comparison between two state of the art algorithms, one for contrastive learning and the other fully supervised, to demonstrate the efficiency gains from contrastive learning. Next, we leverage the notion of temporal coherency which is the notion that frames within an ultrasound cine that are close together share structural similarities. Using data from over 500 patients, our preliminary results indicate that temporal partitioning has potential improvements to the learned embeddings. Future work is needed to investigate the changes to intra-class compactness and inter-class separability for these embeddings, as well as identifying downstream tasks which may benefit the most from temporal coherency.