Deep Spectral Methods for Unsupervised Ultrasound Image Interpretation

TL;DR

This paper introduces a novel unsupervised deep spectral method for ultrasound image segmentation, leveraging self-supervised transformer features and spectral clustering to produce interpretable tissue segments without labeled data.

Contribution

It presents a new unsupervised deep learning approach combining spectral graph theory and transformers specifically tailored for ultrasound image interpretation.

Findings

Outperforms other clustering algorithms in segmentation quality

Maintains boundary accuracy and semantic consistency

Effective across multiple ultrasound datasets

Abstract

Ultrasound imaging is challenging to interpret due to non-uniform intensities, low contrast, and inherent artifacts, necessitating extensive training for non-specialists. Advanced representation with clear tissue structure separation could greatly assist clinicians in mapping underlying anatomy and distinguishing between tissue layers. Decomposing an image into semantically meaningful segments is mainly achieved using supervised segmentation algorithms. Unsupervised methods are beneficial, as acquiring large labeled datasets is difficult and costly, but despite their advantages, they still need to be explored in ultrasound. This paper proposes a novel unsupervised deep learning strategy tailored to ultrasound to obtain easily interpretable tissue separations. We integrate key concepts from unsupervised deep spectral methods, which combine spectral graph theory with deep learning methods. We utilize self-supervised transformer features for spectral clustering to generate meaningful segments based on ultrasound-specific metrics and shape and positional priors, ensuring semantic consistency across the dataset. We evaluate our unsupervised deep learning strategy on three ultrasound datasets, showcasing qualitative results across anatomical contexts without label requirements. We also conduct a comparative analysis against other clustering algorithms to demonstrate superior segmentation performance, boundary preservation, and label consistency.