469 research outputs found
動脈硬化症の診断を目指した超音波による動脈壁の厚みと弾性特性計測の高精度化に関する研究
Tohoku University金井 浩課
Fully automated segmentation and tracking of the intima media thickness in ultrasound video sequences of the common carotid artery
Abstract—The robust identification and measurement of the intima media thickness (IMT) has a high clinical relevance because it represents one of the most precise predictors used in the assessment of potential future cardiovascular events. To facilitate the analysis of arterial wall thickening in serial clinical investigations, in this paper we have developed a novel fully automatic algorithm for the segmentation, measurement, and tracking of the intima media complex (IMC) in B-mode ultrasound video sequences. The proposed algorithm entails a two-stage image analysis process that initially addresses the segmentation of the IMC in the first frame of the ultrasound video sequence using a model-based approach; in the second step, a novel customized tracking procedure is applied to robustly detect the IMC in the subsequent frames. For the video tracking procedure, we introduce a spatially coherent algorithm called adaptive normalized correlation that prevents the tracking process from converging to wrong arterial interfaces. This represents the main contribution of this paper and was developed to deal with inconsistencies in the appearance of the IMC over the cardiac cycle. The quantitative evaluation has been carried out on 40 ultrasound video sequences of the common carotid artery (CCA) by comparing the results returned by the developed algorithm with respect to ground truth data that has been manually annotated by clinical experts. The measured IMTmean ± standard deviation recorded by the proposed algorithm is 0.60 mm ± 0.10, with a mean coefficient of variation (CV) of 2.05%, whereas the corresponding result obtained for the manually annotated ground truth data is 0.60 mm ± 0.11 with a mean CV equal to 5.60%. The numerical results reported in this paper indicate that the proposed algorithm is able to correctly segment and track the IMC in ultrasound CCA video sequences, and we were encouraged by the stability of our technique when applied to data captured under different imaging conditions. Future clinical studies will focus on the evaluation of patients that are affected by advanced cardiovascular conditions such as focal thickening and arterial plaques
Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks
Cardiovascular disease (CVD) is the leading cause of mortality yet largely
preventable, but the key to prevention is to identify at-risk individuals
before adverse events. For predicting individual CVD risk, carotid intima-media
thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable,
offering several advantages over CT coronary artery calcium score. However,
each CIMT examination includes several ultrasound videos, and interpreting each
of these CIMT videos involves three operations: (1) select three end-diastolic
ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI)
in each selected frame, and (3) trace the lumen-intima interface and the
media-adventitia interface in each ROI to measure CIMT. These operations are
tedious, laborious, and time consuming, a serious limitation that hinders the
widespread utilization of CIMT in clinical practice. To overcome this
limitation, this paper presents a new system to automate CIMT video
interpretation. Our extensive experiments demonstrate that the suggested system
significantly outperforms the state-of-the-art methods. The superior
performance is attributable to our unified framework based on convolutional
neural networks (CNNs) coupled with our informative image representation and
effective post-processing of the CNN outputs, which are uniquely designed for
each of the above three operations.Comment: J. Y. Shin, N. Tajbakhsh, R. T. Hurst, C. B. Kendall, and J. Liang.
Automating carotid intima-media thickness video interpretation with
convolutional neural networks. CVPR 2016, pp 2526-2535; N. Tajbakhsh, J. Y.
Shin, R. T. Hurst, C. B. Kendall, and J. Liang. Automatic interpretation of
CIMT videos using convolutional neural networks. Deep Learning for Medical
Image Analysis, Academic Press, 201
Image segmentation and reconstruction of 3D surfaces from carotid ultrasound images
Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200
An automatic 2D CAD algorithm for the segmentation of the IMT in ultrasound carotid artery images
Common carotid intima-media thickness (IMT) is a reliable measure of early atherosclerosis - its accurate measurement can be used in the process of evaluating the presence and tracking the progression of disease. The aim of this study is to introduce a novel unsupervised Computer Aided Detection (CAD) algorithm that is able to identify and measure the IMT in 2D ultrasound carotid images. The developed technique relies on a suite of image processing algorithms that embeds a statistical model to identify the two interfaces that form the IMT without any user intervention. The proposed image segmentation scheme is based on a spatially continuous vascular model and consists of several steps including data preprocessing, edge filtering, model selection, edge reconstruction and data refinement. To conduct a quantitative evaluation each image was manually segmented by clinical experts and performance metrics between the segmentation results obtained by the proposed method and the ground truth data were calculated. The experimental results show that the proposed CAD system is robust in accurately estimating the IMT in ultrasound carotid data
Computer Vision Techniques for Transcatheter Intervention
Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and treatment of cardiovascular diseases. For example, TAVI is an alternative to AVR for the treatment of severe aortic stenosis and TAFA is widely used for the treatment and cure of atrial fibrillation. In addition, catheter-based IVUS and OCT imaging of coronary arteries provides important information about the coronary lumen, wall and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial for the evaluation and treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation, motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods.We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence it is important to understand the application domain, clinical background, and imaging modality so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on background information of transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area
Automated analysis of fibrous cap in intravascular optical coherence tomography images of coronary arteries
Thin-cap fibroatheroma (TCFA) and plaque rupture have been recognized as the
most frequent risk factor for thrombosis and acute coronary syndrome.
Intravascular optical coherence tomography (IVOCT) can identify TCFA and assess
cap thickness, which provides an opportunity to assess plaque vulnerability. We
developed an automated method that can detect lipidous plaque and assess
fibrous cap thickness in IVOCT images. This study analyzed a total of 4,360
IVOCT image frames of 77 lesions among 41 patients. To improve segmentation
performance, preprocessing included lumen segmentation, pixel-shifting, and
noise filtering on the raw polar (r, theta) IVOCT images. We used the
DeepLab-v3 plus deep learning model to classify lipidous plaque pixels. After
lipid detection, we automatically detected the outer border of the fibrous cap
using a special dynamic programming algorithm and assessed the cap thickness.
Our method provided excellent discriminability of lipid plaque with a
sensitivity of 85.8% and A-line Dice coefficient of 0.837. By comparing lipid
angle measurements between two analysts following editing of our automated
software, we found good agreement by Bland-Altman analysis (difference 6.7+/-17
degree; mean 196 degree). Our method accurately detected the fibrous cap from
the detected lipid plaque. Automated analysis required a significant
modification for only 5.5% frames. Furthermore, our method showed a good
agreement of fibrous cap thickness between two analysts with Bland-Altman
analysis (4.2+/-14.6 micron; mean 175 micron), indicating little bias between
users and good reproducibility of the measurement. We developed a fully
automated method for fibrous cap quantification in IVOCT images, resulting in
good agreement with determinations by analysts. The method has great potential
to enable highly automated, repeatable, and comprehensive evaluations of TCFAs.Comment: 18 pages, 9 figure
Classification approach for diagnosis of arteriosclerosis using B-mode ultrasound carotid images
Tese de mestrado. Engenharia Biomédica. Faculdade de Engenharia. Universidade do Porto. 201
- …