scholarly journals An automatic deep learning-based workflow for glioblastoma survival prediction using pre-operative multimodal MR images: a feasibility study

2021 ◽  
pp. 100746
Author(s):  
Jie Fu ◽  
Kamal Singhrao ◽  
Xinran Zhong ◽  
Yu Gao ◽  
Sharon X. Qi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Feasibility Study ◽  
Survival Prediction ◽  
Mr Images

scholarly journals Fully Automated Brain Tumor Segmentation and Survival Prediction of Gliomas using Deep Learning and MRI

2019 ◽  
Author(s):  
Chandan Ganesh Bangalore Yogananda ◽  
Sahil S. Nalawade ◽  
Gowtham K. Murugesan ◽  
Ben Wagner ◽  
Marco C. Pinho ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Texture Features ◽  
Response To Therapy ◽  
Validation Dataset ◽  
Tumor Segmentation ◽  
Survival Prediction ◽  
Accuracy Score ◽  
Mr Images ◽  
Brain Tumor Segmentation

ABSTRACTTumor segmentation of magnetic resonance (MR) images is a critical step in providing objective measures of predicting aggressiveness and response to therapy in gliomas. It has valuable applications in diagnosis, monitoring, and treatment planning of brain tumors. The purpose of this work was to develop a fully automated deep learning method for brain tumor segmentation and survival prediction. Well curated brain tumor cases with multi-parametric MR Images from the BraTS2019 dataset were used. A three-group framework was implemented, with each group consisting of three 3D-Dense-UNets to segment whole tumor (WT), tumor core (TC) and enhancing tumor (ET). This method was implemented to decompose the complex multi-class segmentation problem into individual binary segmentation problems for each sub-component. Each group was trained using different approaches and loss functions. The output segmentations of a particular label from their respective networks from the 3 groups were ensembled and post-processed. For survival analysis, a linear regression model based on imaging texture features and wavelet texture features extracted from each of the segmented components was implemented. The networks were tested on the BraTS2019 validation dataset including 125 cases for the brain tumor segmentation task and 29 cases for the survival prediction task. The segmentation networks achieved average dice scores of 0.901, 0.844 and 0.801 for WT, TC and ET respectively. The survival prediction network achieved an accuracy score of 0.55 and mean squared error (MSE) of 119244. This method could be implemented as a robust tool to assist clinicians in primary brain tumor management and follow-up.

scholarly journals Deep Learning–based Automated Segmentation of the Left Ventricular Trabeculations and Myocardium on Cardiac MR Images: A Feasibility Study

2020 ◽  
pp. e200021
Author(s):  
Axel Bartoli ◽  
Joris Fournel ◽  
Zakarya Bentatou ◽  
Gilbert Habib ◽  
Alain Lalande ◽  
...  
Keyword(s):  
Deep Learning ◽  
Feasibility Study ◽  
Left Ventricular ◽  
Automated Segmentation ◽  
Mr Images ◽  
Cardiac Mr

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

Deep Learning-Based Binary Classification of ADHD Using Resting State MR Images

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Vikas Khullar ◽  
Karuna Salgotra ◽  
Harjit Pal Singh ◽  
Davinder Pal Sharma
Keyword(s):  
Deep Learning ◽  
Resting State ◽  
Binary Classification ◽  
Mr Images

Feasibility Study on Automatic Interpretation of Radiation Dose Using Deep Learning Technique for Dicentric Chromosome Assay

2020 ◽  
Vol 195 (2) ◽  
Author(s):  
Seungsoo Jang ◽  
Sung-Gyun Shin ◽  
Min-Jae Lee ◽  
Sangsoo Han ◽  
Chan-Ho Choi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Radiation Dose ◽  
Feasibility Study ◽  
Dicentric Chromosome ◽  
Automatic Interpretation ◽  
Learning Technique

scholarly journals Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study

2021 ◽  
pp. jnumed.120.256396
Author(s):  
Jaewon Yang ◽  
Luyao Shi ◽  
Rui Wang ◽  
Edward J. Miller ◽  
Albert J. Sinusas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Attenuation Correction ◽  
Feasibility Study ◽  
Cardiac Spect
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