IVUS-Net: An Intravascular Ultrasound Segmentation Network

Abstract

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.

SUMNet: Fully Convolutional Model For Fast Segmentation of Anatomical Structures in Ultrasound Volumes

“…3(d) -(g) and the lumen and external elastic luminae border segmentation in the whole pullback is visualized 1 . The proposed method outperforms the recent related prior art [11] as presented in Table 1. Thyroid Segmentation: The thyroid data used in this experiment has been acquired from a publicly available dataset [3] which includes freehand acquired thyroid US volumes from 16 healthy human subjects imaged with a 11 − 16 MHz probe.…”

“…JCC HD PAD Lumen EEL Lumen EEL Lumen EEL P1 -P8 [8] 0.88 ± 0.05 0.91 ± 0.04 0.34 ± 0.14 0.31 ± 0.12 0.06 ± 0.05 0.05 ± 0.04 IVUS-Net [11] 0.90 ± 0.06 0.86 ± 0.11 0.26 ± 0.25 0.48 ± 0.44 − − 2D SegNet [4] 0.93 ± 0.05 0.89 ± 0.03 0.20 ± 0.13 0.33 ± 0.10 0.07 ± 0.06 0.10 ± 0.06 2D UNet [6] 0.91 ± 0.06 0.88 ± 0.08 0.23 ± 0.19 0.47 ± 0.31 0.10 ± 0.09 0.17 ± 0.12 3D UNet [15] 0.73 ± 0.13 0.76 ± 0.09 0.33 ± 0.17 0.34 ± 0.14 0.21 ± 0.15 0.27 ± 0.11 SUMNet 0.95 ± 0.03 0.97 ± 0.01 0.17 ± 0.07 0.16 ± 0.09 0.01 ± 0.01 0.01 ± 0.01 loss function. Weights are calculated using a morphological distance transform giving higher weights to the pixels closer to the contour as illustrated for the IVUS in Fig.…”

“…In an earlier work [9] making use of backscattering physics based learning model inspired from [2], we had proposed an automated algorithm for segmentation of lumen and external elastic laminae which was subsequently extended to use in both outside-in and inside out imaging in [10]. A fully convolutional network-based architecture, named IVUS-Net [11] is proposed for semantic segmentation of IVUS. Another fully convolutional neural network based architecture based on modified UNet [12] is proposed for semantic segmentation of IVUS and demonstrated only using 40 MHz data.…”

Automatic Segmentation of Coronary Lumen and External Elastic Membrane in IVUS Images Using 8-layer U-Net

“…In recent years, deep learning has been widely applied in the medical imaging analysis and achieved remarkable results [13][14][15]. It has been done to detect the lumen and media-adventitia borders in IVUS due to its capabilities in automatic feature extraction [16][17][18].…”

IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution

“…However, these algorithms assume that regions in the image have a statistically distinctive distribution, which is not always accurate, as IVUS images contain various forms of diseased structures. Among other categories of methods, the graph-based approach [17,18], the Grow-Cut algorithm [19], the nonparametric statistical approach [20], and the deep learning architecture [21,22] were introduced to IVUS image segmentation. The main limitation of deep learning for IVUS image segmentation lies in the lack of enough labeled data, which must be provided by IVUS experts.Despite the considerable attempts that have been devoted to IVUS image segmentation, no approved universal method exists that guarantees a successful segmentation.…”