TY - JOUR
T1 - A learning-based, region of interest-tracking algorithm for catheter detection in echocardiography
AU - Kim, Taeouk
AU - Hedayat, Mohammadali
AU - Vaitkus, Veronica V.
AU - Belohlavek, Marek
AU - Krishnamurthy, Vinayak
AU - Borazjani, Iman
N1 - Funding Information:
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The computational resources for this work were partly provided by the Texas A&M High Performance Research Computing center. The animal studies were supported by the grant EB019947 from the National Institutes of Health to MB.
Funding Information:
This work was supported by the Texas A&M High Performance Research Computing center, USA . The animal studies and development of the acoustically active catheter were supported by the grant EB019947 from the National Institutes of Health, USA .
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/9
Y1 - 2022/9
N2 - Echocardiography (echo) is gaining popularity to guide the catheter during surgical procedures. However, it is difficult to discern the catheter tip in echo even with an acoustically active catheter. An acoustically active catheter is detected for the first time in cardiac echo images using two methods. First, a convolutional neural network (CNN) model was trained to detect the region of interest (ROI), the interior of the left ventricle, containing the catheter tip. Color intensity difference detection technique was implemented on the ROI to detect the catheter. This method succeeded in detecting the catheter without any manual input on 94% and 57% of long- and short-axis projections, respectively. Second, several tracking methods were implemented and tested. Given the manually identified initial positions of the catheter, the tracking methods could distinguish between the target (catheter tip) and the surrounding on the rest of the frames. Combining the two techniques, for the first time, resulted in an automatic, robust, and fast method for catheter detection in echo images.
AB - Echocardiography (echo) is gaining popularity to guide the catheter during surgical procedures. However, it is difficult to discern the catheter tip in echo even with an acoustically active catheter. An acoustically active catheter is detected for the first time in cardiac echo images using two methods. First, a convolutional neural network (CNN) model was trained to detect the region of interest (ROI), the interior of the left ventricle, containing the catheter tip. Color intensity difference detection technique was implemented on the ROI to detect the catheter. This method succeeded in detecting the catheter without any manual input on 94% and 57% of long- and short-axis projections, respectively. Second, several tracking methods were implemented and tested. Given the manually identified initial positions of the catheter, the tracking methods could distinguish between the target (catheter tip) and the surrounding on the rest of the frames. Combining the two techniques, for the first time, resulted in an automatic, robust, and fast method for catheter detection in echo images.
KW - Catheter detection
KW - Deep learning
KW - Echocardiography
KW - Region of interest
KW - Tracking method
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U2 - 10.1016/j.compmedimag.2022.102106
DO - 10.1016/j.compmedimag.2022.102106
M3 - Article
C2 - 35970125
AN - SCOPUS:85135864370
SN - 0895-6111
VL - 100
JO - Computerized Medical Imaging and Graphics
JF - Computerized Medical Imaging and Graphics
M1 - 102106
ER -