Editorial for “Deep Learning Assisted Diagnosis of Musculoskeletal Tumors Based Upon Contrast‐Enhanced Magnetic Resonance Imaging”

2021 ◽  
Author(s):  
Stephan Ellmann ◽  
Tobias Bäuerle
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Resonance Imaging ◽  
Musculoskeletal Tumors ◽  
Contrast Enhanced ◽  
Assisted Diagnosis

scholarly journals Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet

PLoS Medicine ◽  
2018 ◽  
Vol 15 (11) ◽  
pp. e1002699 ◽  
Author(s):  
Nicholas Bien ◽  
Pranav Rajpurkar ◽  
Robyn L. Ball ◽  
Jeremy Irvin ◽  
Allison Park ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Resonance Imaging ◽  
Assisted Diagnosis ◽  
Knee Magnetic Resonance Imaging

scholarly journals Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Qiaoliang Li ◽  
Yuzhen Xu ◽  
Zhewei Chen ◽  
Dexiang Liu ◽  
Shi-Ting Feng ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Nasopharyngeal Carcinoma ◽  
Convolutional Neural Network ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Resonance Imaging ◽  
Contrast Enhanced

Objectives. To evaluate the application of a deep learning architecture, based on the convolutional neural network (CNN) technique, to perform automatic tumor segmentation of magnetic resonance imaging (MRI) for nasopharyngeal carcinoma (NPC). Materials and Methods. In this prospective study, 87 MRI containing tumor regions were acquired from newly diagnosed NPC patients. These 87 MRI were augmented to >60,000 images. The proposed CNN network is composed of two phases: feature representation and scores map reconstruction. We designed a stepwise scheme to train our CNN network. To evaluate the performance of our method, we used case-by-case leave-one-out cross-validation (LOOCV). The ground truth of tumor contouring was acquired by the consensus of two experienced radiologists. Results. The mean values of dice similarity coefficient, percent match, and their corresponding ratio with our method were 0.89±0.05, 0.90±0.04, and 0.84±0.06, respectively, all of which were better than reported values in the similar studies. Conclusions. We successfully established a segmentation method for NPC based on deep learning in contrast-enhanced magnetic resonance imaging. Further clinical trials with dedicated algorithms are warranted.

scholarly journals Deep learning model for glioma, meningioma and pituitary classification

2021 ◽  
Vol 10 (1) ◽  
pp. 88
Author(s):  
Toqa A. Sadoon ◽  
Mohammed H. Ali
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Resonance Imaging ◽  
Contrast Enhanced ◽  
Common Causes ◽  
The Difference ◽  
Magnetic Resonance Imaging Mri ◽  
Confusion Matrices

<p>One of the common causes of death is a brain tumor. Because of the above mentioned, early detection of a brain tumor is critical for faster treatment, and therefore there are many techniques used to visualize a brain tumor. One of these techniques is magnetic resonance imaging (MRI). On the other hand, machine learning, deep learning, and convolutional neural network (CNN) are the state of art technologies in the recent years used in solving many medical image-related problems such as classification. In this research, three types of brain tumors were classified using magnetic resonance imaging namely glioma, meningioma, and pituitary gland on the based of CNN. The dataset used in this work includes 233 patients for a total of 3,064 contrast-enhanced T1 images. In this paper, a comparison is presented between the presented model and other models to demonstrate the superiority of our model over the others. Moreover, the difference in outcome between pre- and post-data preprocessing and augmentation was discussed. The highest accuracy metrics extracted from confusion matrices are; precision of 99.1% for pituitary, sensitivity of 98.7% for glioma, specificity of 99.1%, and accuracy of 99.1% for pituitary. The overall accuracy obtained is 96.1%.</p>

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