LUMEN & MEDIA SEGMENTATION OF IVUS IMAGES VIA ELLIPSE FITTING USING A WAVELET-DECOMPOSED SUBBAND CNN

We propose an automatic segmentation method for both lumen and media in IntraVascular UltraSound (IVUS) images using a deep convolutional neural network (CNN). In contrast to previous approaches that broadly fall under the category of labeling each pixel to be either lumen, media or background, we propose to use a structurally regularized CNN via wavelet-based subband decomposition that directly predicts two ellipses that best represent each of lumen and media segments. The proposed architecture significantly reduces computational complexity and offers better performance compared to recent techniques in the literature. We evaluated our network on the publicly available IVUS-Challenge-2011 dataset using two performance metrics, namely Jaccard Measure (JM) and Hausdorff Distance (HD). The evaluation results show that our proposed network outperforms the state-of-the-art lumen and media segmentation methods by a maximum of 8% in JM (Lumen) and nearly 33% in HD (Media).