Breast lesion co-localisation between X-ray and MR images using finite element modelling-Reference-Cited by-同舟云学术

Breast lesion co-localisation between X-ray and MR images using finite element modelling

Published:2013-12 Issue:8 Volume:17 Page:1256-1264
ISSN:1361-8415
Container-title:Medical Image Analysis
language:en
Short-container-title:Medical Image Analysis

Author:

Lee Angela W.C.,Rajagopal Vijayaraghavan,Babarenda Gamage Thiranja P.,Doyle Anthony J.,Nielsen Poul M.F.,Nash Martyn P.

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference47 articles.

1. Multi-atlas based segmentation pf brain images: atlas selection and its effects on accuracy;Aljabar;NeuroImage,2009

2. A deformable finite element model of the breast for predicting mechanical deformations under external perturbations;Azar;Acad. Radiol.,2001

3. An evaluation of four automatic methods of segmenting the subcortical structures in the brain;Babalola;NeuroImage,2009

4. Fusion of contrast-enhanced breast MR and mammographic imaging data;Behrenbruch;Med. Image Anal.,2003

5. Fusion of contrast-enhanced breast MR and mammographic imaging data;Behrenbruch;Br. J. Radiol.,2004

Cited by 38 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Estimation of the biomechanical mammographic deformation of the breast using machine learning models;Clinical Biomechanics;2023-12

2. Incorporating heterogeneity and anisotropy for surgical applications in breast deformation modeling;Clinical Biomechanics;2023-04

3. Patient-specific biomechanical modeling for applications in breast cancer diagnosis and treatment;Biomechanics of the Female Reproductive System: Breast and Pelvic Organs;2023

4. Mechanical properties of breast tissue;Biomechanics of the Female Reproductive System: Breast and Pelvic Organs;2023

5. Rapid Prediction of Breast Biomechanics Under Gravity Loading Using Surrogate Machine Learning Models;Computational Biomechanics for Medicine;2023