scholarly journals Development and Validation of a Deep Learning–Based Model to Distinguish Glioblastoma from Solitary Brain Metastasis Using Conventional MR Images

2021 ◽  
Author(s):  
I. Shin ◽  
H. Kim ◽  
S.S. Ahn ◽  
B. Sohn ◽  
S. Bae ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Metastasis ◽  
Mr Images ◽  
Solitary Brain Metastasis ◽  
Development And Validation

scholarly journals 64 Automatic Deep Learning Based Segmentation of Brain Metastasis on MPRAGE MR Images for Stereotactic Radiotherapy Planning

2019 ◽  
Vol 139 ◽  
pp. S30
Author(s):  
Allan Fong ◽  
Nevin McVicar ◽  
Alan Nichol ◽  
Carrie-Lynne Swift ◽  
Jordan Wong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Metastasis ◽  
Stereotactic Radiotherapy ◽  
Radiotherapy Planning ◽  
Mr Images

scholarly journals Method for Diagnosing the Bone Marrow Edema of Sacroiliac Joint in Patients with Axial Spondyloarthritis Using Magnetic Resonance Image Analysis Based on Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1156
Author(s):  
Kang Hee Lee ◽  
Sang Tae Choi ◽  
Guen Young Lee ◽  
You Jung Ha ◽  
Sang-Il Choi
Keyword(s):  
Bone Marrow ◽  
Deep Learning ◽  
Bone Marrow Edema ◽  
Axial Spondyloarthritis ◽  
Mr Images ◽  
Sacroiliac Joints ◽  
Learning Network ◽  
Active Sacroiliitis ◽  
Deep Learning Network ◽  
Marrow Edema

Axial spondyloarthritis (axSpA) is a chronic inflammatory disease of the sacroiliac joints. In this study, we develop a method for detecting bone marrow edema by magnetic resonance (MR) imaging of the sacroiliac joints and a deep-learning network. A total of 815 MR images of the sacroiliac joints were obtained from 60 patients diagnosed with axSpA and 19 healthy subjects. Gadolinium-enhanced fat-suppressed T1-weighted oblique coronal images were used for deep learning. Active sacroiliitis was defined as bone marrow edema, and the following processes were performed: setting the region of interest (ROI) and normalizing it to a size suitable for input to a deep-learning network, determining bone marrow edema using a convolutional-neural-network-based deep-learning network for individual MR images, and determining sacroiliac arthritis in subject examinations based on the classification results of individual MR images. About 70% of the patients and normal subjects were randomly selected for the training dataset, and the remaining 30% formed the test dataset. This process was repeated five times to calculate the average classification rate of the five-fold sets. The gradient-weighted class activation mapping method was used to validate the classification results. In the performance analysis of the ResNet18-based classification network for individual MR images, use of the ROI showed excellent detection performance of bone marrow edema with 93.55 ± 2.19% accuracy, 92.87 ± 1.27% recall, and 94.69 ± 3.03% precision. The overall performance was additionally improved using a median filter to reflect the context information. Finally, active sacroiliitis was diagnosed in individual subjects with 96.06 ± 2.83% accuracy, 100% recall, and 94.84 ± 3.73% precision. This is a pilot study to diagnose bone marrow edema by deep learning based on MR images, and the results suggest that MR analysis using deep learning can be a useful complementary means for clinicians to diagnose bone marrow edema.

scholarly journals DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia from MR Images

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Suriya Murugan ◽  
Chandran Venkatesan ◽  
M G Sumithra ◽  
Xiao-Zhi Gao ◽  
B Elakkiya ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Diagnosis ◽  
Learning Model ◽  
Mr Images ◽  
Deep Learning Model
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