IntraVascular UltraSound (IVUS) is one of the most effective imaging modalities that provides assistance to experts in order to diagnose and treat cardiovascular diseases. We address a central problem in IVUS image analysis with Fully Convolutional Network (FCN): automatically delineate the lumen and media-adventitia borders in IVUS images, which is crucial to shorten the diagnosis process or benefits a faster and more accurate 3D reconstruction of the artery. Particularly, we propose an FCN architecture, called IVUS-Net, followed by a post-processing contour extraction step, in order to automatically segments the interior (lumen) and exterior (media-adventitia) regions of the human arteries. We evaluated our IVUS-Net on the test set of a standard publicly available dataset containing 326 IVUS B-mode images with two measurements, namely Jaccard Measure (JM) and Hausdorff Distances (HD). The evaluation result shows that IVUS-Net outperforms the state-of-the-art lumen and media segmentation methods by 4% to 20% in terms of HD distance. IVUS-Net performs well on images in the test set that contain a significant amount of major artifacts such as bifurcations, shadows, and side branches that are not common in the training set. Furthermore, using a modern GPU, IVUS-Net segments each IVUS frame only in 0.15 seconds. The proposed work, to the best of our knowledge, is the first deep learning based method for segmentation of both the lumen and the media vessel walls in 20 MHz IVUS B-mode images that achieves the best results without any manual intervention. Code is available at https://github.com/ Kulbear/ivus-segmentation-icsm2018.
A problem of computer vision applications is to detect regions of interest under different imaging conditions. The state-of-the-art maximally stable extremal regions (MSERs) detects affine covariant regions by applying all possible thresholds on the input image, and through three main steps including: (1) making a component tree of extremal regions' evolution; (2) obtaining region stability criterion; and (3) cleaning up. The MSER performs very well, but, it does not consider any information about the boundaries of the regions, which are important for detecting repeatable extremal regions. We have shown in this paper that employing prior information about boundaries of regions results in a novel region detector algorithm that not only outperforms MSER, but avoids the MSER's rather complicated steps of enumeration and the cleaning up. To employ the information about the region boundaries, we introduce maxima of gradient magnitudes (MGMs) which are shown to be points that are mostly around the boundaries of the regions. Having found the MGMs, the method obtains a global criterion for each level of the input image which is used to find extremum levels (ELs). The found ELs are then used to detect extremal regions. The proposed algorithm which is called extremal regions of extremum levels (EREL) has been tested on the public benchmark data set of Mikolajczyk. The obtained experimental results show that the inclusion of region boundaries through MGMs, results in a detector that detects regions with high repeatability scores and is more robust against noise compared with MSER.
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