Deep Spectral Methods for Unsupervised Ultrasound Image Interpretation
This work addresses the challenge of ultrasound image interpretation for clinicians by providing an unsupervised method that avoids costly labeled datasets, though it appears incremental as it combines existing spectral and deep learning techniques.
The paper tackles the problem of interpreting ultrasound images by proposing an unsupervised deep learning strategy that integrates spectral graph theory with transformer features to achieve tissue separation without labeled data. The method demonstrates superior segmentation performance, boundary preservation, and label consistency compared to other clustering algorithms on three ultrasound datasets.
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.