ArticleBiomedical optics express2023
Automated pipeline for breast cancer diagnosis using US assisted diffuse optical tomography.
Article in Biomedical optics express, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Convolutional-transformer fusion of ultrasound and diffuse optical tomography for breast lesion classification.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2026Article
- Ultrasound-guided diffuse optical tomography: an adjunct to ultrasound that can reduce unnecessary breast biopsies.Breast cancer research : BCR · 2025Article
- Improving diffuse optical tomography imaging quality using APU-Net: an attention-based physical U-Net model.Journal of biomedical optics · 2024Article
- Real-time breast lesion classification combining diffuse optical tomography frequency domain data and BI-RADS assessment.Journal of biophotonics · 2024Article
- Self-Guided Algorithm for Fast Image Reconstruction in Photo-Magnetic Imaging: Artificial Intelligence-Assisted Approach.Bioengineering (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Ultrasound (US)-guided diffuse optical tomography (DOT) is a portable and non-invasive 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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Registered trials
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