Carotid plaque segmentation from three-dimensional ultrasound images by direct three-dimensional sparse field level-set optimization. (1st March 2018)

• Record Type: Journal Article
• Authors: Cheng, Jieyu; Chen, Yimin; Yu, Yanyan; Chiu, Bernard
• Is Part Of: Computers in biology and medicine. Volume 94(2018)
• Page Start: 27
• Page End: 40
• Publication Date: 2018-03-01
• Subjects: 3D carotid ultrasound -- Carotid atherosclerosis -- Plaque segmentation -- Direct 3D level-set segmentation; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2018.01.002
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Total plaque volume (TPV) measured from 3D carotid ultrasound has been shown to be able to predict cardiovascular events and is sensitive in detecting treatment effects. Manual plaque segmentation was performed in previous studies to quantify TPV, but is tedious, requires long training times and is prone to observer variability. This article introduces the first 3D direct volume-based level-set algorithm to segment plaques from 3D carotid ultrasound images. The plaque surfaces were first initialized based on the lumen and outer wall boundaries generated by a previously described semi-automatic algorithm and then deformed by a direct three-dimensional sparse field level-set algorithm, which enforced the longitudinal continuity of the segmented plaque surfaces. This is a marked advantage as compared to a previously proposed 2D slice-by-slice plaque segmentation method. In plaque boundary initialization, the previous technique performed a search on lines connecting corresponding point pairs of the outer wall and lumen boundaries. A limitation of this initialization strategy was that an inaccurate initial plaque boundary would be generated if the plaque was not enclosed entirely by the wall and lumen boundaries. A mechanism is proposed to extend the search range in order to capture the entire plaque if the outer wall boundary lies on a weak edge in the 3D ultrasound image. The proposed method was compared with the previously described 2D slice-by-slice plaque segmentation method in 26 three-dimensional carotid ultrasound images containing 27 plaques with volumes ranging from 12.5 to 450.0 m m 3 . The manually segmented plaque boundaries serve as the surrogate gold standard. Segmentation accuracy was quantified by volume-, area- and distance-based metrics, including absolute plaque volume difference ( | Δ P V | ), Dice similarity coefficient (DSC), mean and maximum absolute distance (MAD and MAXD). The proposed direct 3D plaque segmentation algorithm was associated with a significantly lower | Δ P V |, MAD and MAXD, and a significantly higher DSC compared to the previously described slice-by-slice algorithm ( | Δ P V | : p = 0.012, DSC: p = 2.1 × 10 − 4, MAD: p = 1.3 × 10 − 4, MAXD: p = 5.2 × 10 − 4 ). The proposed 3D volume-based algorithm required 72 ± 22 s to segment a plaque, which is 40% lower than the 2D slice-by-slice algorithm ( 114 ± 18 s). The proposed automatic plaque segmentation method generates accurate and reproducible boundaries efficiently and will allow for streamlining plaque quantification based on 3D ultrasound images.