3 research outputs found
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
Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging
[EN] Glaucoma is a neurodegenerative disease process that leads to progressive damage of the optic nerve to produce visual impairment and blindness. Spectral-domain OCT technology enables peripapillary circular scans of the retina and the measurement of the thickness of the retinal nerve fiber layer (RNFL) for the assessment of the disease status or progression in glaucoma patients. This paper describes a new approach to segment and measure the retinal nerve fiber layer in peripapillary OCT images. The proposed method consists of two stages. In the first one, morphological operators robustly detect the coarse location of the layer boundaries, despite the speckle noise and diverse artifacts in the OCT image. In the second stage, deformable models are initialized with the results of the previous stage to perform a fine segmentation of the boundaries, providing an accurate measurement of the entire RNFL. The results of the RNFL segmentation were qualitatively assessed by ophthalmologists, and the measurements of the thickness of the RNFL were quantitatively compared with those provided by the OCT inbuilt software as well as the state-of-the-art methods.This work was partially funded by Spanish National projects AES2017-PI17/00771 and AES2017-PI17/00821 (Instituto de Salud Carlos III), PID2019-105142RB-C21 (AI4SKIN) (Spanish Ministry of Economy and Competitiveness), PTA2017-14610-I (State Research Spanish Agency), regional project 20901/PI/18 (Fundacion Seneca) and Polytechnic University of Valencia (PAID-01-20).Berenguer-Vidal, R.; VerdĂş-Monedero, R.; Morales-Sánchez, J.; SellĂ©s-Navarro, I.; Del Amor, R.; GarcĂa-Pardo, JG.; Naranjo Ornedo, V. (2021). Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging. Sensors. 21(23):1-30. https://doi.org/10.3390/s21238027S130212
Frequency-domain active contours solution to evaluate intima–media thickness of the common carotid artery
The intima–media thickness (IMT) of the common carotid artery (CCA) is being used as a reliable and early detector of atherosclerosis. Atherosclerosis may be unnoticed fo years before triggering severe illnesses such as stroke, embolisms or ischemia. Hence, the use of IMT leads to an early atherosclerosis diagnosis that can prevent more serious cardiovascular diseases. Usually, IMT is manually extracted from ultrasound images, which is a non-invasive technique, but unfortunately its measurement is prone to error. This paper addresses a fully automatic method to segment the artery layers of the CCA over ultrasound images. Unlike other methods, the segmentation is not restricted to IMT, the artery diameter can be extracted too, which can help to determine cardiovascular risk together with IMT. The proposed technique is based on a frequency-domain implementation of active contours, which are computationally faster than the original space-formulation, while providing soft final contours. Working with three different probes over a range of spatial resolutions from 0.029mm/pixel to 0.081mm/pixel, the method presents an IMT error of only 13.8±31.9μm (in mean±standard deviation) when tested on an database containing 46 images. The automatic results were compared to the average of 2 manual observations performed by 2 observers (4 observations) over each image in our database