Journal
ULTRASOUND IN MEDICINE AND BIOLOGY
Volume 46, Issue 10, Pages 2819-2833Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ultrasmedbio.2020.06.015
Keywords
Breast ultrasound; Medical image segmentation; Visual saliency; Domain knowledge-enriched learning
Funding
- Center for Modeling Complex Interactions (CMCI) at the University of Idaho through National Institutes of Health [P20GM104420]
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Incorporating human domain knowledge for breast tumor diagnosis is challenging because shape, boundary, curvature, intensity or other common medical priors vary significantly across patients and cannot be employed. This work proposes a new approach to integrating visual saliency into a deep learning model for breast tumor segmentation in ultrasound images. Visual saliency refers to image maps containing regions that are more likely to attract radiologists' visual attention. The proposed approach introduces attention blocks into a U-Net architecture and learns feature representations that prioritize spatial regions with high saliency levels. The validation results indicate increased accuracy for tumor segmentation relative to models without salient attention layers. The approach achieved a Dice similarity coefficient (DSC) of 90.5% on a data set of 510 images. The salient attention model has the potential to enhance accuracy and robustness in processing medical images of other organs, by providing a means to incorporate task-specific knowledge into deep learning architectures. (C) 2020 World Federation for Ultrasound in Medicine & Biology. All rights reserved.
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