An artificial neural network method for lumen and media-adventitia border detection in IVUS. (April 2017)

• Record Type: Journal Article
• Authors: Su, Shengran; Hu, Zhenghui; Lin, Qiang; Hau, William Kongto; Gao, Zhifan; Zhang, Heye
• Is Part Of: Computerized medical imaging and graphics. Volume 57(2017)
• Page Start: 29
• Page End: 39
• Publication Date: 2017-04
• Subjects: Intravascular image -- Image segmentation -- Artificial neural network -- Sparse auto-encoders; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2016.11.003
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: Used the artificial neural network (ANN) method as the feature learning algorithm, we got accurate results with simple feature input. The error of our approach is close to the error of two manual trace results from two IVUS experts. We did the Leave-one-out cross-validation, which proved the stability and adaptability of our approach. Abstract: Intravascular ultrasound (IVUS) has been well recognized as one powerful imaging technique to evaluate the stenosis inside the coronary arteries. The detection of lumen border and media-adventitia (MA) border in IVUS images is the key procedure to determine the plaque burden inside the coronary arteries, but this detection could be burdensome to the doctor because of large volume of the IVUS images. In this paper, we use the artificial neural network (ANN) method as the feature learning algorithm for the detection of the lumen and MA borders in IVUS images. Two types of imaging information including spatial, neighboring features were used as the input data to the ANN method, and then the different vascular layers were distinguished accordingly through two sparse auto-encoders and one softmax classifier. Another ANN was used to optimize the result of the first network. In the end, the active contour model was applied to smooth the lumen and MA borders detected by the ANN method. The performance of our approach was compared with the manual drawing method performed by two IVUS experts on 461 IVUS images from four subjects. Results showed that our approach had a high correlation and good agreement with the manual drawing results. The detection error of the ANN method close to the error between two groups of manual drawing result. All these results indicated that our proposed approach could efficiently and accurately handle the detection of lumen and MA borders in the IVUS images.