Keywords: ultrasound, augmentation, acoustic shadow, speckle, segmentation
TL;DR: We use acoustic shadows and a new "colourspace" for ultrasound to augment images in machine learning.
Abstract: Data augmentation techniques are frequently used to prevent overfitting, enhance generalizability, and overcome limited amounts of data. This data-oriented approach commonly includes domain-agnostic techniques of geometric transformations, colour space changes, and generative adversarial networks. However, leveraging domain-specific traits in aug- mentations may yield further improvements. We propose three new contributions to ultrasound augmentation: zoom, artificial shadowing, and speckle parameter maps. We first present zoom, a modification on scale which maintains the ultrasound beam shape. We then characterize acoustic shadows within abdominal ultrasound images, and formulate a method to introduce artificial shadows in a realistic manner into existing images. Finally, we transform B-mode ultrasound images into speckle parameter maps based on the Nakagami distribution to represent spatial structures not obvious in conventional B-mode. The three augmentations are evaluated in training a fully supervised network and a contrastive learning network for multi-class intra-organ semantic segmentation. Our preliminary results demonstrate the benefit of using zoom and speckle maps as augmentation, and the challenges presented by acoustic shadowing, in segmentation.
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Paper Type: both
Primary Subject Area: Application: Radiology
Secondary Subject Area: Segmentation
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Code And Data: https://github.com/rsingla92/speckle_n_shadow