Improved Reproducibility in Measuring the Laminar Thickness on Enhanced Depth Imaging SD-OCT Images Using Maximum Intensity Projection

2012 ◽  
Vol 53 (12) ◽  
pp. 7576 ◽  
Author(s):  
Eun Ji Lee ◽  
Tae-Woo Kim ◽  
Robert N. Weinreb
Keyword(s):  
Maximum Intensity Projection ◽  
Maximum Intensity ◽  
Enhanced Depth Imaging ◽  
Depth Imaging ◽  
Laminar Thickness ◽  
Sd Oct

scholarly journals The Application of Enhanced Depth Imaging Spectral-Domain Optical Coherence Tomography in Macular Diseases

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Jing Wang ◽  
Lian-Rong Yin
Keyword(s):  
Optical Coherence Tomography ◽  
Choroidal Thickness ◽  
Spectral Domain ◽  
Enhanced Depth Imaging ◽  
Optical Coherence ◽  
Cross Sectional ◽  
Depth Imaging ◽  
Diagnosis And Prognosis ◽  
Macular Diseases ◽  
Sd Oct

The choroid plays an essential role in the pathogenesis of various posterior segment diseases. However, traditional imaging methods still have limited cross-sectional observation of choroid. Enhanced depth imaging in spectral-domain optical coherence tomography (EDI SD-OCT) uses a closer scanning position to the eye to create an inverted SD-OCT image with the advantage of better depth sensitivity, which can observe choroidal structure and measure choroidal thickness (CT) accurately. At present, more and more choroidal thickness measurements have been made in normal and pathologic states, in order to understand the pathogenesis and differential diagnosis and prognosis of various diseases, especially for macular lesions. This paper would review relevant original literatures published from January 1, 2008, to February 1, 2020, to evaluate the relationship between the changes of CT with EDI SD-OCT and macular diseases.

scholarly journals Enhanced Depth Imaging of Central Laminar Thickness in Optic Neuropathy: Comparison with Normal Eyes

2015 ◽  
Vol 39 (4) ◽  
pp. 166-174
Author(s):  
Suntaree Thitiwichienlert ◽  
Hitoshi Ishikawa ◽  
Ken Asakawa ◽  
Tetsuya Ikeda ◽  
Kimiya Shimizu
Keyword(s):  
Optic Neuropathy ◽  
Enhanced Depth Imaging ◽  
Depth Imaging ◽  
Laminar Thickness

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.

Dual-sensor data and enhanced depth imaging sheds new light onto the mature Viking Graben area

First Break ◽  
2016 ◽  
Vol 34 (9) ◽  
Author(s):  
Marielle Ciotoli ◽  
Sophie Beaumont ◽  
Julien Oukili ◽  
Øystein Korsmo ◽  
Nicola O'Dowd ◽  
...  
Keyword(s):  
Sensor Data ◽  
Enhanced Depth Imaging ◽  
Depth Imaging ◽  
Dual Sensor
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