Automatic Detection and Quantification of Calcium Objects from Clinical Images for Risk Level Assessment of Coronary Disease

2017 ◽  
pp. 213-225
Author(s):  
R. Priyatharshini ◽  
S. Chitrakala
Keyword(s):  
Coronary Disease ◽  
Automatic Detection ◽  
Risk Level ◽  
Clinical Images ◽  
Detection And Quantification

scholarly journals Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R

2021 ◽  
Vol 273 ◽  
pp. 116490
Author(s):  
Odei Garcia-Garin ◽  
Toni Monleón-Getino ◽  
Pere López-Brosa ◽  
Asunción Borrell ◽  
Alex Aguilar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Web Application ◽  
Automatic Detection ◽  
Aerial Images ◽  
Learning Approach ◽  
Detection And Quantification

Development of a deep learning-based algorithm for the automatic detection and quantification of aortic valve calcium

2021 ◽  
Vol 137 ◽  
pp. 109582
Author(s):  
Suyon Chang ◽  
Hwiyoung Kim ◽  
Young Joo Suh ◽  
Dong Min Choi ◽  
Hyunghu Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Aortic Valve ◽  
Automatic Detection ◽  
Detection And Quantification

Automatic detection and quantification of coronary calcium on 3D CT angiography data

2010 ◽  
Vol 26 (1-2) ◽  
pp. 117-124 ◽  
Author(s):  
Matthias Teßmann ◽  
Fernando Vega-Higuera ◽  
Bernhard Bischoff ◽  
Jörg Hausleiter ◽  
Günther Greiner
Keyword(s):  
Ct Angiography ◽  
Automatic Detection ◽  
Coronary Calcium ◽  
3D Ct ◽  
3D Ct Angiography ◽  
Detection And Quantification

Automatic detection and quantification of sleep apnea using heart rate variability

2010 ◽  
Vol 43 (6) ◽  
pp. 535-541 ◽  
Author(s):  
Saeed Babaeizadeh ◽  
David P. White ◽  
Stephen D. Pittman ◽  
Sophia H. Zhou
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Apnea ◽  
Automatic Detection ◽  
Detection And Quantification

A deep Residual U-Net algorithm for automatic detection and quantification of ascites on the abdominopelvic computed tomography acquired in the emergency department (Preprint)

2021 ◽  
Author(s):  
Hoon Ko ◽  
Jimi Huh ◽  
Kyung Won Kim ◽  
Heewon Chung ◽  
Yousun Ko ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Automatic Detection ◽  
High Specificity ◽  
Ct Images ◽  
Free Fluid ◽  
Detection Accuracy ◽  
Abdominopelvic Ct ◽  
Segmentation Accuracy ◽  
Detection And Quantification

BACKGROUND Detection and quantification of intraabdominal free fluid (i.e., ascites) on computed tomography (CT) are essential processes to find emergent or urgent conditions in patients. In an emergent department, automatic detection and quantification of ascites will be beneficial. OBJECTIVE We aimed to develop an artificial intelligence (AI) algorithm for the automatic detection and quantification of ascites simultaneously using a single deep learning model (DLM). METHODS 2D deep learning models (DLMs) based on a deep residual U-Net, U-Net, bi-directional U-Net, and recurrent residual U-net were developed to segment areas of ascites on an abdominopelvic CT. Based on segmentation results, the DLMs detected ascites by classifying CT images into ascites images and non-ascites images. The AI algorithms were trained using 6,337 CT images from 160 subjects (80 with ascites and 80 without ascites) and tested using 1,635 CT images from 40 subjects (20 with ascites and 20 without ascites). The performance of AI algorithms was evaluated for diagnostic accuracy of ascites detection and for segmentation accuracy of ascites areas. Of these DLMs, we proposed an AI algorithm with the best performance. RESULTS The segmentation accuracy was the highest in the deep residual U-Net with a mean intersection over union (mIoU) value of 0.87, followed by U-Net, bi-directional U-Net, and recurrent residual U-net (mIoU values 0.80, 0.77, and 0.67, respectively). The detection accuracy was the highest in the deep residual U-net (0.96), followed by U-Net, bi-directional U-net, and recurrent residual U-net (0.90, 0.88, and 0.82, respectively). The deep residual U-net also achieved high sensitivity (0.96) and high specificity (0.96). CONCLUSIONS We propose the deep residual U-net-based AI algorithm for automatic detection and quantification of ascites on abdominopelvic CT scans, which provides excellent performance.

An Algorithm for the Automatic Detection and Quantification of Athletes’ Change of Direction Incidents Using IMU Sensor Data

2019 ◽  
Vol 19 (12) ◽  
pp. 4518-4527 ◽  
Author(s):  
Mahir Meghji ◽  
Aaron Balloch ◽  
Daryoush Habibi ◽  
Iftekhar Ahmad ◽  
Nicolas Hart ◽  
...  
Keyword(s):  
Automatic Detection ◽  
Sensor Data ◽  
Change Of Direction ◽  
Detection And Quantification

scholarly journals CystAnalyser: A new software tool for the automatic detection and quantification of cysts in Polycystic Kidney and Liver Disease, and other cystic disorders

2020 ◽  
Vol 16 (10) ◽  
pp. e1008337
Author(s):  
Adrián Cordido ◽  
Eva Cernadas ◽  
Manuel Fernández-Delgado ◽  
Miguel A. García-González
Keyword(s):  
Liver Disease ◽  
Automatic Detection ◽  
Software Tool ◽  
Polycystic Kidney ◽  
Detection And Quantification

scholarly journals Automatic Detection and Quantification of Abdominal Aortic Calcification in Dual Energy X-ray Absorptiometry

2016 ◽  
Vol 96 ◽  
pp. 1011-1021 ◽  
Author(s):  
Karima Elmasri ◽  
Yulia Hicks ◽  
Xin Yang ◽  
Xianfang Sun ◽  
Rebecca Pettit ◽  
...  
Keyword(s):  
Automatic Detection ◽  
Dual Energy ◽  
Aortic Calcification ◽  
Abdominal Aortic Calcification ◽  
X Ray ◽  
Abdominal Aortic ◽  
Detection And Quantification
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