Semantic segmentation with DenseNets for carotid artery ultrasound plaque segmentation and CIMT estimation
Maria Del Mar Vila1, Beatriz Remeseiro2, Maria Grau3
1CIBER Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Monforte de Lemos 3-5, Pabellón 11, 28029 Madrid, Spain; IMIM, Institut Hospital del Mar d'Investigacions Mèdiques, Dr. Aiguader 88, 08003 Barcelona, Spain; Departament de Matemà tiques i Informà tica, Universitat de Barcelona, Gran Via de les Corts Catalanes 585, 08007 Barcelona, Spain.
Background And Objective:
The measurement of carotid intima media thickness (CIMT) in ultrasound images can be used to detect the presence of atherosclerotic plaques. Usually, the CIMT estimation strategy is semi-automatic, since it requires: (1) a manual examination of the ultrasound image for the localization of a region of interest (ROI), a fast and useful operation when only a small number of images need to be measured; and (2) an automatic delineation of the CIM region within the ROI. The existing efforts for automating the process have replicated the same two-step structure, resulting in two consecutive independent approaches. In this work, we propose a fully automatic single-step approach based on semantic segmentation that allows us to segment the plaque and to estimate the CIMT in a fast and useful manner for large data sets of images.
Methods:
Our single-step approach is based on densely connected convolutional neural networks (DenseNets) for semantic segmentation of the whole image. It has two remarkable characteristics: (1) it avoids ROI definition, and (2) it captures multi-scale contextual information in the complete image interpretation, due to the concatenation of feature maps carried out in DenseNets. Once the input image is segmented, a straightforward method for CIMT estimation and plaque detection is applied.
Results:
The proposed method has been validated with a large data set (REGICOR) of more than 8000 images, corresponding to two territories of the carotid artery: common carotid artery (CCA) and bulb. Among them, a subset of 331 images has been used to evaluate the performance of semantic segmentation (≈90% for train, ≈10% for test). The experimental results demonstrated that our method outperforms other deep models and shallow approaches found in the literature. In particular, our CIMT estimation reaches a correlation coefficient of 0.81, and a CIMT mean error of 0.02 and 0.06 mm in CCA and Bulb images, respectively. Furthermore, the accuracy for plaque detection is 96.45% and 78.09% in CCA and Bulb, respectively. To test the generalization power, the method has also been tested with another data set (NEFRONA) that includes images acquired with different equipment.
Conclusions:
The validation carried out demonstrates that the proposed method is accurate and objective for both plaque detection and CIMT measurement. Moreover, the robustness and generalization capacity of the method have been proven with two different data sets.
