Automated segmental-IMT measurement in thin/thick plaque with bulb presence in carotid ultrasound from multiple scanners: Stroke risk assessment. (April 2017)

• Record Type: Journal Article
• Authors: Ikeda, Nobutaka; Dey, Nilanjan; Sharma, Aditya; Gupta, Ajay; Bose, Soumyo; Acharjee, Suvojit; Shafique, Shoaib; Cuadrado-Godia, Elisa; Araki, Tadashi; Saba, Luca; Laird, John R.; Nicolaides, Andrew; Suri, Jasjit S.
• Is Part Of: Computer methods and programs in biomedicine. Volume 141(2017)
• Page Start: 73
• Page End: 81
• Publication Date: 2017-04
• Subjects: Carotid ultrasound -- Stroke risk assessment -- Heavy plaque -- Segmental-IMT -- bIMT -- cIMT; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.01.009
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Automated detection of the carotid bulb edge, which is considered a reference marker for measurements of the cIMT. Automated segment-based cIMT measurement system which estimates the cIMT for different segments of the carotid artery proximal to the bulb edge. Segmental-IMT (sIMT) allows us to measure IMT in 10 mm segments (namely: s1, s2 and s3) proximal to the bulb edge. The proposed fully automated bulb detection system achieved 92.67% precision against ideal bulb edge locations in the bulb transition zone and holds a significant promise for risk stratification tool for carotid disease. Abstract: Background and objectives: Standardization of the carotid IMT requires a reference marker in ultrasound scans. It has been shown previously that manual reference marker and manually created carotid segments are used for measuring IMT in these segments. Manual methods are tedious, time consuming, subjective, and prone to errors. Bulb edge can be considered as a reference marker for measurements of the cIMT. However, bulb edge can be difficult to locate in ultrasound scans due to: (a) low signal to noise ratio in the bulb region as compared to common carotid artery region; (b) uncertainty of bulb location in craniocaudal direction; and (c) variability in carotid bulb shape and size. This paper presents an automated system (a class of AtheroEdge™ system from AtheroPoint™, Roseville, CA, USA) for locating the bulb edge as a reference marker and further develop segmental-IMT (sIMT) which measures IMT in 10 mm segments (namely: s1, s2 and s3) proximal to the bulb edge. Methods: The patented methodology uses an integrated approach which combines carotid geometry and pixel-classification paradigms. The system first finds the bulb edge and then measures the sIMT proximal to the bulb edge. The system also estimates IMT in bulb region (bIMT). The 649 image database consists of varying plaque (light, moderate to heavy), image resolutions, shapes, sizes and ethnicity. Results: Our results show that the IMT contributions in different carotid segments are as follows: bulb-IMT 34%, s1-IMT 29.46%, s2-IMT 11.48%, and s3-IMT 12.75%, respectively. We compare our automated results against reader's tracings demonstrating the following performance: mean lumen-intima error: 0.01235 ± 0.01224 mm, mean media-adventitia error: 0.020933 ± 0.01539 mm and mean IMT error: 0.01063 ± 0.0031 mm. Our system's Precision of Merit is: 98.23%, coefficient of correlation between automated and Reader's IMT is: 0.998 ( p -value < 0.0001). These numbers are improved compared to previous publications by Suri's group which is automated multi-resolution conventional cIMT. Conclusions: Our fully automated bulb detection system reports 92.67% precision against ideal bulb edge locations as marked by the reader in the bulb transition zone.