Key result
A fully automatic Kalman filter snake approach accurately segmented carotid intima-media borders, with segmentation errors of 32.1 ± 37.5 µm for LI and 35.0 ± 41.5 µm for MA compared to manual tracing.
A novel fully automatic Kalman filter snake method accurately segments carotid intima-media borders on ultrasound sequences, showing strong agreement with manual physician delineations.
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May streamline carotid ultrasound analysis; leaves open prospective clinical validation before adoption.
Segmentation of carotid intima-media (IM) borders from ultrasound images is of great importance for predicting cardiovascular risks. In this paper, we have developed a fully automatic approach to sequentially segment the carotid IM borders in each image throughout ultrasound sequences. First, the first frame of an ultrasound sequence is automatically segmented using edge detectors and dynamic programming, and then the rest frames are segmented successively under the state-space framework. Under this framework, we developed a variant of the snake method for a precise measurement. The evaluation of our segmentation result is done by comparison with average manual delineations of three physicians on a total of 65 sequences. The accuracy of our method is high. (Segmentation error is32.1~± ~37.5~μ mfor LI and35.0~± ~41.5~μ mfor MA.) The BA plot and the linear regression also demonstrate that our method is in agreement with the ground truth. This paper strengthens the potential of the state-space and snake-based approach in segmenting IM borders for clinical diagnosis by demonstrating a fully automatic scheme.
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Zhao et al. (2018) studied Carotid intima-media border segmentation (n=65). Fully automatic approach using Kalman filter snake vs. Manual delineations by three physicians was evaluated on Segmentation error. A fully automatic Kalman filter snake approach accurately segmented carotid intima-media borders, with segmentation errors of 32.1 ± 37.5 µm for LI and 35.0 ± 41.5 µm for MA compared to manual tracing.
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