Machine learning in breast MRI
Machine‐learning techniques have led to remarkable advances in data extraction and
analysis of medical imaging. Applications of machine learning to breast MRI continue to …
analysis of medical imaging. Applications of machine learning to breast MRI continue to …
Automated segmentation of tissues using CT and MRI: a systematic review
Rationale and Objectives The automated segmentation of organs and tissues throughout the
body using computed tomography and magnetic resonance imaging has been rapidly …
body using computed tomography and magnetic resonance imaging has been rapidly …
Artificial intelligence for breast MRI in 2008–2018: a systematic mapping review
M Codari, S Schiaffino, F Sardanelli… - American Journal of …, 2019 - Am Roentgen Ray Soc
OBJECTIVE. The purpose of this study is to review literature from the past decade on
applications of artificial intelligence (AI) to breast MRI. MATERIALS AND METHODS. In June …
applications of artificial intelligence (AI) to breast MRI. MATERIALS AND METHODS. In June …
Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas‐aided fuzzy C‐means method
Purpose: Breast magnetic resonance imaging (MRI) plays an important role in the clinical
management of breast cancer. Studies suggest that the relative amount of fibroglandular (ie …
management of breast cancer. Studies suggest that the relative amount of fibroglandular (ie …
Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs
Accurate segmentation of the breast region of interest (BROI) and breast density (BD) is a
significant challenge during the analysis of breast MR images. Most of the existing methods …
significant challenge during the analysis of breast MR images. Most of the existing methods …
Automatic outer and inner breast tissue segmentation using multi-parametric MRI images of breast tumor patients
S Thakran, S Chatterjee, M Singhal, RK Gupta… - PLoS …, 2018 - journals.plos.org
The objectives of the study were to develop a framework for automatic outer and inner breast
tissue segmentation using multi-parametric MRI images of the breast tumor patients; and to …
tissue segmentation using multi-parametric MRI images of the breast tumor patients; and to …
Development of U-net breast density segmentation method for fat-sat MR images using transfer learning based on non-fat-sat model
Y Zhang, S Chan, JH Chen, KT Chang, CY Lin… - Journal of digital …, 2021 - Springer
To develop a U-net deep learning method for breast tissue segmentation on fat-sat T1-
weighted (T1W) MRI using transfer learning (TL) from a model developed for non-fat-sat …
weighted (T1W) MRI using transfer learning (TL) from a model developed for non-fat-sat …
Machine learning based on multi-parametric MRI to predict risk of breast cancer
W Tao, M Lu, X Zhou, S Montemezzi, G Bai… - Frontiers in …, 2021 - frontiersin.org
Purpose Machine learning (ML) can extract high-throughput features of images to predict
disease. This study aimed to develop nomogram of multi-parametric MRI (mpMRI) ML model …
disease. This study aimed to develop nomogram of multi-parametric MRI (mpMRI) ML model …
Automated breast segmentation of fat and water MR images using dynamic programming
JA Rosado-Toro, T Barr, JP Galons, MT Marron… - Academic radiology, 2015 - Elsevier
Rationale and Objectives To develop and test an algorithm that outlines the breast
boundaries using information from fat and water magnetic resonance images. Materials and …
boundaries using information from fat and water magnetic resonance images. Materials and …
Comprehensive computer‐aided diagnosis for breast T1‐weighted DCE‐MRI through quantitative dynamical features and spatio‐temporal local binary patterns
Dynamic contrast enhanced‐magnetic resonance imaging (DCE‐MRI) is a valid
complementary diagnostic method for early detection and diagnosis of breast cancer …
complementary diagnostic method for early detection and diagnosis of breast cancer …