Three-dimensional segmentation of breast masses from digital breast tomosynthesis images
Author:
Affiliation:
1. University of Manchester, Division of Informatics Imaging and Data Sciences, School of Health Scienc
2. University Hospital of South Manchester, Nightingale Breast Centre, Manchester
Publisher
SPIE-Intl Soc Optical Eng
Subject
Radiology, Nuclear Medicine and imaging
Reference49 articles.
1. The Royal College of Radiologists Breast Group breast imaging classification
2. Primary breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up
3. A review of automatic mass detection and segmentation in mammographic images
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1. Machine learning in breast cancer imaging: a review on data, models and methods;Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization;2024-01-19
2. Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies;Journal of Cancer Research and Clinical Oncology;2023-06-06
3. Artificial intelligence for digital breast tomosynthesis: Impact on diagnostic performance, reading times, and workload in the era of personalized screening;European Journal of Radiology;2023-01
4. Mass Detection and Segmentation in Digital Breast Tomosynthesis Using 3D-Mask Region-Based Convolutional Neural Network: A Comparative Analysis;Frontiers in Molecular Biosciences;2020-11-11
5. Monte Carlo study on optimal breast voxel resolution for dosimetry estimates in digital breast tomosynthesis;Physics in Medicine & Biology;2018-12-19
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