Computational Platform Based on Deep Learning for Segmenting Ventricular Endocardium in Long-axis Cardiac MR Imaging

2018 ◽  
Author(s):  
Shuang Leng ◽  
Xulei Yang ◽  
Xiaodan Zhao ◽  
Zeng Zeng ◽  
Yi Su ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mr Imaging ◽  
Cardiac Mr ◽  
Computational Platform

scholarly journals Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2866
Author(s):  
Fernando Navarro ◽  
Hendrik Dapper ◽  
Rebecca Asadpour ◽  
Carolin Knebel ◽  
Matthew B. Spraker ◽  
...  
Keyword(s):  
Deep Learning ◽  
Soft Tissue ◽  
Mr Imaging ◽  
Characteristic Curve ◽  
External Validation ◽  
Soft Tissue Sarcomas ◽  
Treatment Decision ◽  
Receiver Operator Characteristic Curve ◽  
Low Grade ◽  
Tumor Grading

Background: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. Methods: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. Results: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. Conclusions: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.

Sensitivity of Myocardial Radiomic Features to Imaging Parameters in Cardiac MR Imaging

2021 ◽  
Author(s):  
Jihye Jang ◽  
Hossam El‐Rewaidy ◽  
Long H. Ngo ◽  
Jennifer Mancio ◽  
Ibolya Csecs ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Cardiac Mr ◽  
Imaging Parameters

scholarly journals Cardiac MR segmentation based on sequence propagation by deep learning

PLoS ONE ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. e0230415
Author(s):  
Chao Luo ◽  
Canghong Shi ◽  
Xiaoji Li ◽  
Dongrui Gao
Keyword(s):  
Deep Learning ◽  
Cardiac Mr ◽  
Mr Segmentation

scholarly journals Adverse Left Ventricular Remodeling and Age Assessed with Cardiac MR Imaging: The Multi-Ethnic Study of Atherosclerosis

Radiology ◽  
2016 ◽  
Vol 278 (3) ◽  
pp. 714-722 ◽  
Author(s):  
John Eng ◽  
Robyn L. McClelland ◽  
Antoinette S. Gomes ◽  
W. Gregory Hundley ◽  
Susan Cheng ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Ventricular Remodeling ◽  
Left Ventricular Remodeling ◽  
Left Ventricular ◽  
Ethnic Study ◽  
Cardiac Mr

scholarly journals Acute Myocarditis: Multiparametric Cardiac MR Imaging

Radiology ◽  
2014 ◽  
Vol 273 (2) ◽  
pp. 383-392 ◽  
Author(s):  
Julian A. Luetkens ◽  
Jonas Doerner ◽  
Daniel K. Thomas ◽  
Darius Dabir ◽  
Juergen Gieseke ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Acute Myocarditis ◽  
Cardiac Mr

scholarly journals Quantification of Myocardial Extracellular Volume Fraction with Cardiac MR Imaging in Thalassemia Major

Radiology ◽  
2016 ◽  
Vol 279 (3) ◽  
pp. 720-730 ◽  
Author(s):  
Kate Hanneman ◽  
Elsie T. Nguyen ◽  
Paaladinesh Thavendiranathan ◽  
Richard Ward ◽  
Andreas Greiser ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Volume Fraction ◽  
Extracellular Volume ◽  
Extracellular Volume Fraction ◽  
Thalassemia Major ◽  
Cardiac Mr
Sign in / Sign up

Export Citation Format

Share Document