Journal
BIOMEDICAL OPTICS EXPRESS
Volume 14, Issue 11, Pages 6072-6087Publisher
Optica Publishing Group
DOI: 10.1364/BOE.502244
Keywords
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In this study, an automated ultrasound (US)-assisted diffuse optical tomography (DOT) pipeline was developed for fast and accurate diagnosis. The pipeline includes automated DOT pre-processing, imaging reconstruction, and final diagnosis based on US imaging features and DOT measurements. This approach significantly reduces the processing time while maintaining comparable classification results with manual processing.
Ultrasound (US)-guided diffuse optical tomography (DOT) is a portable and noninvasive imaging modality for breast cancer diagnosis and treatment response monitoring. However, DOT data pre-processing and imaging reconstruction often require labor intensive manual processing which hampers real-time diagnosis. In this study, we aim at providing an automated US-assisted DOT pre-processing, imaging and diagnosis pipeline to achieve near real-time diagnosis. We have developed an automated DOT pre-processing method including motion detection, mismatch classification using deep-learning approach, and outlier removal. US-lesion information needed for DOT reconstruction was extracted by a semi-automated lesion segmentation approach combined with a US reading algorithm. A deep learning model was used to evaluate the quality of the reconstructed DOT images and a two-step deep-learning model developed earlier is implemented to provide final diagnosis based on US imaging features and DOT measurements and imaging results. The presented US-assisted DOT pipeline accurately processed the DOT measurements and reconstruction and reduced the procedure time to 2 to 3 minutes while maintained a comparable classification result with manually processed dataset.
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