An artificial neural network method for lumen and media-adventitia border detection in IVUS
Introduction
Atherosclerosis is a disease of the vessel wall, and it is known as the major cause of cardiovascular diseases such as heart attack or stroke (Frostegard, 2005). Stenosis in coronary artery caused by the atherosclerosis might lead to clinical complications like angina, myocardial infarction, and even sudden cardiac death. Cardiovascular diseases are receiving more attention because they account for 30% of all deaths worldwide (Morabia, 2005). Intravascular Ultrasound (IVUS) technique has been widely applied for diagnosing the arteriosclerotic disease of coronary artery in this decade (Brunenberg et al., 2006) because of its capability to visualize the inner structure of blood vessel in real time (Ciompi et al., 2009, Ciompo et al., 2011). IVUS allows monitoring and quantifying the state of the vessel wall and lumen and it is an intra-operative imaging tool for the quantification and characterization of coronary plaque, used for diagnostic purposes and for guiding Percutaneous Coronary Intervention (PCI), enabling the visualization of high resolution images of internal vascular structures. Compared with other angiographic imaging modalities, (e.g. X-ray, MRA and CT), IVUS enables the visualization of both vessel morphology and plaque. It provides extremely high image resolution (up to 113 um), which make it become the only modality enabling the accurate morphological segmentation of both vessel membranes (lumen and media) and the assessment of the plaque type in vivo (Nair et al., 2002, Sathyaranayana et al., 2009). An important task in the IVUS diagnosis is to determine the plaque burden, which requires the location of lumen border and the media-adventitia (MA) borders of blood vessel (Mendizabal-Ruiz et al., 2009). It is of substantial clinical interest and contributes to clinical decision making. Yet, no truly reliable and consistently accurate IVUS segmentation methods exist that would guarantee segmentation success in a clinical setting. Because of high volume of IVUS images, it could be very challenging to process the IVUS image in real time if the medical physicians need to identify these two borders by hand. Furthermore, high inter-observer and intra observer variability (up to 20%) might occur in the medical physicians without enough experiences (Meier et al., 1997). Therefore, it is a great demand to develop the computer- aided approach to detect the lumen and MA borders for efficient diagnostic and treatment of arteriosclerotic disease. Automated lumen segmentation of IVUS sequences has been a topic of interest since the early 1990s. Some approaches were based on the use of local properties of the image such as pixel intensity and gradient information (edges) combined with computational methods including graph search (Zhang et al., 1998, Sonka et al., 1995), active surfaces (Klingensmith et al., 2000), active contours (Kovalski et al., 2000), and in other approaches, segmentation was accomplished by the use of gray level variances to model ultrasound speckle (Luo et al., 2003), contrast of regions (Hui-Zhu et al., 2002), statistical properties of the image (Brusseau and de Korte, 2004, Gil et al., 2006), spatiotemporal information (3D segmentation) (Roy Cardinal et al., 2006), and discrete wavelet decomposition (dos Santos Filho et al., 2006). Most of previous attempts to detect the lumen and MA borders could be divided into three categories: active contour-based method, edge-based method, and probability-based method. The snake and the level set were two commonly used active contour-based methods for border detection (Katouzian et al., 2012), however, the contour evolution was heavily depend on the prior shape constraints of blood vessel. In the contrast, the edge-based method detects the borders by analyzing the appearance of edges in the IVUS images (Essa et al., 2011). The region-based method detects the borders by distinguishing the lumen region, plaque region and adventitia region using appearances difference among the three regions (Mendizabal-Ruiz and Kakadiaris, 2012). Nevertheless, the performance of these three methods highly relied on sophisticated features that might only work for specific problem. For example, in the active contour-based method, gradient vector flow is usually to deal with the problem of boundary concavities (Xu and Prince, 1998). For the edge-based method and region-based method, it is challenging to design proper edge features or region features in order to recognize the lumen and MA interfaces. It is of great demand to detect the lumen and MA borders using general imaging features, such as pixel and gradient information. In this work, we apply the artificial neural network (ANN) method to learn the hidden structures and connections of general imaging features and transform them into a more efficient representation. We use two types of data including spatial information, neighborhood pixels information as the input to the ANN. Our approach has been tested on a set of 461 IVUS images from four subjects by comparing to the manual drawing method.
Section snippets
Methodology
As one popular algorithm in the classification and regression problems, the ANN method can extract the hidden structures and connections among these image features by learning their compressed representation. In this study, we applied the ANN method to learn imaging features in order to detect the lumen and media-adventitia borders in IVUS images (Hilton and Salakhutdinov, 2006). After learning imaging features of different vessel layers using the ANN method, we could identify the borders by
Experiments and results
In order to validate the performance of our approach, our approach was compared to the manual drawing method on 461 IVUS images from four subjects. Fig. 5 displayed some example results of deteting the lumen and media-adventitia borders. The following content will explain the details of all the experiments in this work.
Data Collection: All the IVUS data were collected using a commercially available IVUS machine (Inision Gold, Volcano) with a 20 MHz solid-state IVUS catheter (EagleEye Volcano).
Discussion
Our method has a high accuracy. As shown in Table 1, the LCSA Correlation of Testing data is 99.047%, and manual compare value is 98.42%. The VCSA Correlation of Testing data is 96.48%, and manual compare value is 98.19%. The result is very close, which means the error between our approach and the manual trace, is closed to the error between the two manual trace results from the two IVUS experts. The PCSA Correlation of Testing data is 86.479%, and manual compare value is 94.253%. PCSA is more
Conclusion
In this study, we developed a double ANN approach to detect the lumen and MA borders from IVUS images. Unlike other methods (Yan et al., 2014, Sofian et al., 2015), our method does not require any special human feature extraction and calibration. It has extremely simple initialization, strong adaptability, good robustness, and high accuracy. The snak process play a role in the smoothing curve. The proposed approach has been evaluated on 461 IVUS images acquired from four subjects by different
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant 81271645, Guangdong Image-guided Therapy Innovation Team (2011S013), and the national Key research and Development Program of China (2016YFC1300300).
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2021, Computerized Medical Imaging and GraphicsCitation Excerpt :Firstly, we train two separate single-class binary segmentation models for the Lumen and Media. This mirrors the approach taken in previous work (Su et al., 2017; Yang et al., 2018) with separate networks trained for extraction of the Lumen and Media. We evaluate our architectural improvements in this binary setting.
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Shengran Su and Zhenghui Hu contributed equally to the writing of this article.