A self‐supervised strategy for fully automatic segmentation of renal dynamic contrast‐enhanced magnetic resonance images

2019 ◽  
Vol 46 (10) ◽  
pp. 4417-4430
Author(s):  
Wenjian Huang ◽  
Hao Li ◽  
Rui Wang ◽  
Xiaodong Zhang ◽  
Xiaoying Wang ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Automatic Segmentation ◽  
Magnetic Resonance Images ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Fully Automatic

scholarly journals Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 330
Author(s):  
Mio Adachi ◽  
Tomoyuki Fujioka ◽  
Mio Mori ◽  
Kazunori Kubota ◽  
Yuka Kikuchi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Diagnostic Performance ◽  
Maximum Intensity Projection ◽  
Breast Mri ◽  
Magnetic Resonance Images ◽  
Maximum Intensity ◽  
Validation Data ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

scholarly journals An open source software for analysis of dynamic contrast enhanced magnetic resonance images: UMMPerfusion revisited

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Frank G. Zöllner ◽  
Markus Daab ◽  
Steven P. Sourbron ◽  
Lothar R. Schad ◽  
Stefan O. Schoenberg ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Open Source ◽  
Open Source Software ◽  
Magnetic Resonance Images ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced
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