  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.
