Snakes Based Segmentation of the Common Carotid Artery Intima Media

Overview

Journal Med Biol Eng Comput

Publisher Springer

Specialties Biomedical Engineering
Medical Informatics

Date 2007 Jan 5

PMID 17203319

Citations 37

Authors

C P Loizou

C S Pattichis

M Pantziaris

T Tyllis

A Nicolaides

Affiliations

Soon will be listed here.

Abstract

Ultrasound measurements of the human carotid artery walls are conventionally obtained by manually tracing interfaces between tissue layers. In this study we present a snakes segmentation technique for detecting the intima-media layer of the far wall of the common carotid artery (CCA) in longitudinal ultrasound images, by applying snakes, after normalization, speckle reduction, and normalization and speckle reduction. The proposed technique utilizes an improved snake initialization method, and an improved validation of the segmentation method. We have tested and clinically validated the segmentation technique on 100 longitudinal ultrasound images of the carotid artery based on manual measurements by two vascular experts, and a set of different evaluation criteria based on statistical measures and univariate statistical analysis. The results showed that there was no significant difference between all the snakes segmentation measurements and the manual measurements. For the normalized despeckled images, better snakes segmentation results with an intra-observer error of 0.08, a coefficient of variation of 12.5%, best Bland-Altman plot with smaller differences between experts (0.01, 0.09 for Expert1 and Expert 2, respectively), and a Hausdorff distance of 5.2, were obtained. Therefore, the pre-processing of ultrasound images of the carotid artery with normalization and speckle reduction, followed by the snakes segmentation algorithm can be used successfully in the measurement of IMT complementing the manual measurements. The present results are an expansion of data published earlier as an extended abstract in IFMBE Proceedings (Loizou et al. IEEE Int X Mediterr Conf Medicon Med Biol Eng POS-03 499:1-4, 2004).

Citing Articles

Ultrasound-Based Image Analysis for Predicting Carotid Artery Stenosis Risk: A Comprehensive Review of the Problem, Techniques, Datasets, and Future Directions.

Ottakath N, Al-Maadeed S, Zughaier S, Elharrouss O, Mohammed H, Chowdhury M Diagnostics (Basel). 2023; 13(15).

PMID: 37568976 PMC: 10417708. DOI: 10.3390/diagnostics13152614.

Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm.

Jain P, Dubey A, Saba L, Khanna N, Laird J, Nicolaides A J Cardiovasc Dev Dis. 2022; 9(10).

PMID: 36286278 PMC: 9604424. DOI: 10.3390/jcdd9100326.

Reconstruction of carotid stenosis hemodynamics based on guidewire pressure data and computational modeling.

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PMID: 35359199 PMC: 9036983. DOI: 10.1007/s11517-021-02463-2.

Deep Learning for Carotid Plaque Segmentation using a Dilated U-Net Architecture.

Meshram N, Mitchell C, Wilbrand S, Dempsey R, Varghese T Ultrason Imaging. 2020; 42(4-5):221-230.

PMID: 32885739 PMC: 8045553. DOI: 10.1177/0161734620951216.

Improved Deep Learning Network Based in combination with Cost-sensitive Learning for Early Detection of Ovarian Cancer in Color Ultrasound Detecting System.

Zhang L, Huang J, Liu L J Med Syst. 2019; 43(8):251.

PMID: 31254110 DOI: 10.1007/s10916-019-1356-8.

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