Ultrasound-inspired Adaptations for Multi-class Contrastive Segmentation

Rohit Singla, Cailin Ringstrom, Victoria Lessoway, Janice Reid, Robert Rohling, Chris Nguan

Keywords: ultrasound, temporal coherency, speckle, contrastive learning, segmentation

TL;DR: We use the time domain of ultrasound and speckle noise modelling to improve multi-class segmentation.

Abstract: Creating ground truth segmentations for medical imaging is labour and time-intensive. Contrastive learning techniques have shown promise in previous work on magnetic resonance, computed tomography and X-ray imaging. We investigate the potential benefits of using ultrasound-inspired adaptations to the contrastive learning paradigm. Specifically, we investigate the novel concepts of temporal coherency and a speckle loss. We first perform a head-to-head label efficiency comparison between two state of the art algorithms, one that is fully supervised and the other that is contrastive. Next, we leverage temporal coherency, the notion that frames within an ultrasound video clip that are close together share structural similarities. Finally, we explore a loss function based on the Nakagami probability distribution in order to provide a speckle noise constraint on the learned embeddings. Using diverse kidney ultrasound data, our preliminary results indicate that temporal partitioning has potential improvements to segmentation accuracy, whereas speckle-based loss does not. Future work will investigate changes to intra-class compactness and inter-class separability.

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Paper Type: validation/application paper

Primary Subject Area: Application: Radiology

Secondary Subject Area: Segmentation

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