title: CAUDLES-EF: carotid automated ultrasound double line extraction system using edge flow.
abstract: The evaluation of the carotid artery wall is essential for the diagnosis of cardiovascular pathologies or for the assessment of a patient's cardiovascular risk. This paper presents a completely user-independent algorithm, which automatically extracts the far double line (lumen-intima and media-adventitia) in the carotid artery using an Edge Flow technique based on directional probability maps using the attributes of intensity and texture. Specifically, the algorithm traces the boundaries between the lumen and intima layer (line one) and between the media and adventitia layer (line two). The Carotid Automated Ultrasound Double Line Extraction System based on Edge-Flow (CAUDLES-EF) is characterized and validated by comparing the output of the algorithm with the manual tracing boundaries carried out by three experts. We also benchmark our new technique with the two other completely automatic techniques (CALEXia and CULEXsa) we previously published. Our multi-institutional database consisted of 300 longitudinal B-mode carotid images with normal and pathologic arteries. We compared our current new method with previous methods, and showed the mean and standard deviation for the three methods: CALEXia, CULEXsa, and CAUDLES-EF as 0.134 ± 0.088, 0.074 ± 0.092, and 0.043 ± 0.097 mm, respectively. Our IMT was slightly underestimated with respect to the ground truth IMT, but showed a uniform behavior over the entire database. Regarding the Figure of Merit (FoM), CALEXia and CULEXsa showed the values of 84.7% and 91.5%, respectively, while our new approach, CAUDLES-EF, performed the best at 94.8%, showing a good improvement compared to previous methods.
authors.affiliation: Biolab-Dipartimento di Elettronica, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129, Torino, Italy. filippo.molinari@polito.it
authors.forename: Filippo
authors.initials: F
authors.lastname: Molinari
authors.forename: Kristen M
authors.initials: KM
authors.lastname: Meiburger
authors.forename: Guang
authors.initials: G
authors.lastname: Zeng
authors.forename: Andrew
authors.initials: A
authors.lastname: Nicolaides
authors.forename: Jasjit S
authors.initials: JS
authors.lastname: Suri
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002318:Cardiovascular Diseases / Q000000981:diagnostic imaging* / Q000473:pathology; D002339:Carotid Arteries / Q000000981:diagnostic imaging* / Q000473:pathology; D003238:Connective Tissue / Q000000981:diagnostic imaging / Q000473:pathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D018570:Risk Assessment / Q000379:methods; D017539:Tunica Intima / Q000000981:diagnostic imaging / Q000473:pathology; D014463:Ultrasonography