Peer-review started: July 21, 2019
First decision: September 21, 2019
Revised: October 11, 2019
Accepted: November 20, 2019
Article in press: November 20, 2019
Published online: January 28, 2020
Segmentation of arterial vessels is an important step is the assessment of vascular disease. For many years, the accepted method of producing segmentations was through manual approach performed by expert researchers. We apply the technique of convolutional neural networks (CNNs) to the task of segmentation of carotid arteries and compare the results to the manual method.
The accepted standard of manual segmentation by expert researchers is an onerous and time-consuming task that is inherently subjective. Consequently, constructing an algorithm from such an opaque process is problematic. Creation and adoption of a reliable segmentation algorithm could lead to significant savings through automation.
The objective in this study was to examine the feasibility of applying CNNs to the task of segmenting carotid arteries of subjects with vascular disease.
Subsets of magnetic resonance images of the carotid arteries of 189 subjects with atherosclerotic disease were used to train and subsequently validate the CNN. Image segmentations used to train the CNN were produced by an expert reader who manually segmented individual images of the carotid wall using conventional means resulting in a dataset of 4422 segmented images. In preparation for automated segmentation, the original dataset was divided into 3 groups: A “training dataset” (3581 images), a “validation dataset” (398 images), and a “test dataset” (443 images). These datasets were used to train two separate segmentation CNNs (one for carotid lumen and the other for carotid wall). After training, images from the test dataset were processed to produce segmentations as binary images.
Overall quantitative assessment between manual and automated segmentations was determined by computing the DICE coefficient for each pair of segmented images in the test dataset. The average DICE coefficient between automated and manual segmentations was 0.87 for the carotid vessel wall and 0.96 for the carotid lumen. Intra-class correlation coefficients (ICC) as well as Pearson correlation values were computed for vessel area metrics as determined for the expert reader and the CNN to assess the agreement of measurements. Excellent agreement was observed in the segmentation of lumen area (Pearson correlation = 0.98, ICC = 0.98) as well as in the segmentation of vessel wall area (Pearson correlation = 0.88, ICC = 0.86). Additionally, Bland-Altman plots of these measurements for the CNN and reader indicate good agreement.
In this study, we have demonstrated the effectiveness of CNN technology in its application to the task of delineating carotid vessel walls thereby facilitating the detection of potential pathology.
Although the technique produces reasonable results that are on par with expert human assessments, in our application it requires human supervision and monitoring to ensure consistent results. We intend to deploy this algorithm as part of a software platform to lessen researchers workload to more quickly obtain reliable results.