The automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis. In this work we present our attempt to automate the challenging task of measuring the vascular diameter of the fet al abdominal aorta from ultrasound images. We propose a neural network architecture consisting of three blocks: a convolutional layer for the extraction of imaging features, a Convolution Gated Recurrent Unit (C-GRU) for enforcing the temporal coherence across video frames and exploiting the temporal redundancy of a signal, and a regularized loss function, called , to impose our prior knowledge about the periodicity of the observed signal. We present experimental evidence suggesting that the proposed architecture can reach an accuracy substantially superior to previously proposed methods, providing an average reduction of the mean squared error from @math (state-of-art) to @math , and a relative error reduction from @math to @math . The mean execution speed of the proposed approach of 289 frames per second makes it suitable for real time clinical use.
The interest for measuring the diameter and intima-media thickness (IMT) of major vessels has stemmed from its importance as biomarker of hypertension damage and atherosclerosis in adults. Typically, the IMT is assessed on the carotid artery by identifying its lumen and the different layers of its wall on high resolution US images. The improvements provided by the design of semi-automatic and automatic methods based mainly on the image intensity profile, distribution and gradients analysis, and more recently on active contours. For a comprehensive review of these classical methods we refer the reader to @cite_11 and @cite_14 . In the prenatal setting, the lower image quality, due to the need of imaging deeper in the mother's womb and by the movement of the fetus, makes the measurement of a IMT biomarker, although measured on the abdominal aorta, challenging.
@cite_14 @cite_11
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The determination of the wall thickness [intima–media thickness (IMT)], the delineation of the atherosclerotic carotid plaque, the measurement of the diameter in the common carotid artery (CCA), as well as the grading of its stenosis are important for the evaluation of the atherosclerosis disease. All these measurements are also considered to be significant markers for the clinical evaluation of the risk of stroke. A number of CCA segmentation techniques have been proposed in the last few years either for the segmentation of the intima–media complex (IMC), the lumen of the CCA, or for the atherosclerotic carotid plaque from ultrasound images or videos of the CCA. The present review study proposes and discusses the methods and systems introduced so far in the literature for performing automated or semi-automated segmentation in ultrasound images or videos of the CCA. These are based on edge detection, active contours, level sets, dynamic programming, local statistics, Hough transform, statistical modeling, neural networks, and an integration of the above methods. Furthermore, the performance of these systems is evaluated and discussed based on various evaluation metrics. We finally propose the best performing method that can be used for the segmentation of the IMC and the atherosclerotic carotid plaque in ultrasound images and videos. We end the present review study with a discussion of the different image and video CCA segmentation techniques, future perspectives, and further extension of these techniques to ultrasound video segmentation and wall tracking of the CCA. Future work on the segmentation of the CCA will be focused on the development of integrated segmentation systems for the complete segmentation of the CCA as well as the segmentation and motion analysis of the plaque and or the IMC from ultrasound video sequences of the CCA. These systems will improve the evaluation, follow up, and treatment of patients affected by advanced atherosclerosis disease conditions. Last 10 years have witnessed the growth of many computer applications for the segmentation of the vessel wall in ultrasound imaging. Epidemiological studies showed that the thickness of the major arteries is an early and effective marker of onset of cardiovascular diseases. Ultrasound imaging, being real-time, economic, reliable, safe, and now seems to become a standard in vascular assessment methodology. This review is an attempt to discuss the most performing methodologies that have been developed so far to perform computer-based segmentation and intima-media thickness (IMT) measurement of the carotid arteries in ultrasound images. First we will present the rationale and the clinical relevance of computer-based measurements in clinical practice, followed by the challenges that one has to face when approaching the segmentation of ultrasound vascular images. The core of the paper is the presentation, discussion, benchmarking and evaluation of different segmentation techniques, including: edge-detection, active contours, dynamic programming, local statistics, Hough transform, statistical modeling, and integration of these approaches. Also, we will discuss and compare the different performance metrics that have been proposed and used to perform the validation. Best performing user-dependent techniques show an average IMT measurement error of about [email protected] when compared to human tracings [57], whereas completely automated techniques show errors of about [email protected] The review ends with a discussion about the current standards in carotid wall segmentation and in an overview of the future perspectives, which may include the adoption of advanced and intelligent strategies to let the computer technique measure the IMT in the image portion where measurement is more reliable. Author(s): Choi, Gary PT; Chiu, Bernard; Rycroft, Chris H | Abstract: Carotid atherosclerosis is a focal disease at the bifurcations of the carotid artery. To quantitatively monitor the local changes in the vessel-wall-plus-plaque thickness (VWT) and compare the VWT distributions for different patients or for the same patients at different ultrasound scanning sessions, a mapping technique is required to adjust for the geometric variability of different carotid artery models. In this work, we propose a novel method called density-equalizing reference map (DERM) for mapping 3D carotid surfaces to a standardized 2D carotid template, with an emphasis on preserving the local geometry of the carotid surface by minimizing the local area distortion. The initial map was generated by a previously described arc-length scaling (ALS) mapping method, which projects a 3D carotid surface onto a 2D non-convex L-shaped domain. A smooth and area-preserving flattened map was subsequently constructed by deforming the ALS map using the proposed algorithm that combines the density-equalizing map and the reference map techniques. This combination allows, for the first time, one-to-one mapping from a 3D surface to a standardized non-convex planar domain in an area-preserving manner. Evaluations using 20 carotid surface models show that the proposed method reduced the area distortion of the flattening maps by over 80 as compared to the ALS mapping method. Purpose: To assess if segmentation of the aorta can be accurately achieved using the modulus image of phase contrast (PC) magnetic resonance (MR) acquisitions. Materials and Methods: PC image sequences containing both the ascending and descending aorta of 52 subjects were acquired using three different MR scanners. An automated segmentation technique, based on a 2D+t deformable surface that takes into account the features of PC aortic images, such as flow-related effects, was developed. The study was designed to: 1) assess the variability of our approach and its robustness to the type of MR scanner, and 2) determine its sensitivity to aortic dilation and its accuracy against an expert manual tracing. Results: Interobserver variability in the lumen area was 0.59 ± 0.92 for the automated approach versus 10.09 ± 8.29 for manual segmentation. The mean Dice overlap measure was 0.945 ± 0.014. The method was robust to the aortic size and highly correlated (r = 0.99) with the manual tracing in terms of aortic area and diameter. Conclusion: A fast and robust automated segmentation of the aortic lumen was developed and successfully tested on images provided by various MR scanners and acquired on healthy volunteers as well as on patients with a dilated aorta. J. Magn. Reson. Imaging 2010;31:881–888. ©2010 Wiley-Liss, Inc. State-of-the-art Machine Translation (MT) systems are still far from being perfect. An alternative is the so-called Interactive Machine Translation (IMT) framework. In this framework, the knowledge of a human translator is combined with a MT system. The vast majority of the existing work on IMT makes use of the well-known batch learning paradigm. In the batch learning paradigm, the training of the IMT system and the interactive translation process are carried out in separate stages. This paradigm is not able to take advantage of the new knowledge produced by the user of the IMT system. In this paper, we present an application of the online learning paradigm to the IMT framework. In the online learning paradigm, the training and prediction stages are no longer separated. This feature is particularly useful in IMT since it allows the user feedback to be taken into account. The online learning techniques proposed here incrementally update the statistical models involved in the translation process. Empirical results show the great potential of online learning in the IMT framework. Purpose: To investigate the potential of the normalized probabilistic atlases and computer-aided medical image analysis to automatically segment and quantify livers and spleens for extracting imaging biomarkers (volume and height). Methods: A clinical tool was developed to segment livers and spleen from 257 abdominal contrast-enhanced CT studies. There were 51 normal livers, 44 normal spleens, 128 splenomegaly, 59 hepatomegaly, and 23 partial hepatectomy cases. 20 more contrast-enhanced CT scans from a public site with manual segmentations of mainly pathological livers were used to test the method. Data were acquired on a variety of scanners from different manufacturers and at varying resolution. Probabilistic atlases of livers and spleens were created using manually segmented data from ten noncontrast CT scans (five male and five female). The organ locations were modeled in the physical space and normalized to the position of an anatomical landmark, the xiphoid. The construction and exploitation of liver and spleen atlases enabled the automated quantifications of liver spleen volumes and heights (midhepatic liver height and cephalocaudal spleen height) from abdominal CT data. The quantification was improved incrementally by a geodesic active contour, patient specific contrast-enhancement characteristics passed to an adaptive convolution, and correction for shape and location errors. Results: The livers and spleens were robustly segmented from normal and pathological cases. For the liver, the Dice Tanimoto volume overlaps were 96.2 92.7 , the volume height errors were 2.2 2.8 , the root-mean-squared error (RMSE) was 2.3 mm, and the average surface distance (ASD) was 1.2 mm. The spleen quantification led to 95.2 91 Dice Tanimoto overlaps, 3.3 1.7 volume height errors, 1.1 mm RMSE, and 0.7 ASD. The correlations ( R 2 ) with clinical manual height measurements were 0.97 and 0.93 for the spleen and liver, respectively ( p 0.0001 ) . No significant difference ( p > 0.2 ) was found comparing interobserver and automatic-manual volume height errors for liver and spleen. Conclusions: The algorithm is robust to segmenting normal and enlarged spleens and livers, and in the presence of tumors and large morphological changes due to partial hepatectomy. Imaging biomarkers of the liver and spleen from automated computer-assisted tools have the potential to assist the diagnosis of abdominal disorders from routine analysis of clinical data and guide clinical management. The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each segmentation result is not feasible at large scale. However, it is important to be able to detect when an automatic method fails to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions. To overcome this challenge, we explore an approach for predicting segmentation quality based on reverse classification accuracy, which enables us to discriminate between successful and failed cases. We validate this approach on a large cohort of cardiac MRI for which manual QC scores were available. Our results on 7,425 cases demonstrate the potential for fully automatic QC in the context of large-scale population imaging such as the UK Biobank Imaging Study. This paper presents a framework to automatically measure the intensity of naturally occurring facial actions. Naturalistic expressions are non-posed spontaneous actions. The facial action coding system (FACS) is the gold standard technique for describing facial expressions, which are parsed as comprehensive, nonoverlapping action units (Aus). AUs have intensities ranging from absent to maximal on a six-point metric (i.e., 0 to 5). Despite the efforts in recognizing the presence of non-posed action units, measuring their intensity has not been studied comprehensively. In this paper, we develop a framework to measure the intensity of AU12 (lip corner puller) and AU6 (cheek raising) in videos captured from infant-mother live face-to-face communications. The AU12 and AU6 are the most challenging case of infant's expressions (e.g., low facial texture in infant's face). One of the problems in facial image analysis is the large dimensionality of the visual data. Our approach for solving this problem is to utilize the spectral regression technique to project high dimensionality facial images into a low dimensionality space. Represented facial images in the low dimensional space are utilized to train support vector machine classifiers to predict the intensity of action units. Analysis of 18 minutes of captured video of non-posed facial expressions of several infants and mothers shows significant agreement between a human FACS coder and our approach, which makes it an efficient approach for automated measurement of the intensity of non-posed facial action units. Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed up. In this paper, we present a deep neural network that is specifically designed to provide high resolution 3-D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artifacts. Due to the high complexity of the photoacoustic forward operator, we separate training and computation of the gradient information. A suitable prior for the desired image structures is learned as part of the training. The resulting network is trained and tested on a set of segmented vessels from lung computed tomography scans and then applied to in-vivo photoacoustic measurement data. The article introduces digital image restoration to the reader who is just beginning in this field, and provides a review and analysis for the reader who may already be well-versed in image restoration. The perspective on the topic is one that comes primarily from work done in the field of signal processing. Thus, many of the techniques and works cited relate to classical signal processing approaches to estimation theory, filtering, and numerical analysis. In particular, the emphasis is placed primarily on digital image restoration algorithms that grow out of an area known as "regularized least squares" methods. It should be noted, however, that digital image restoration is a very broad field, as we discuss, and thus contains many other successful approaches that have been developed from different perspectives, such as optics, astronomy, and medical imaging, just to name a few. In the process of reviewing this topic, we address a number of very important issues in this field that are not typically discussed in the technical literature. The use of Machine Translation as a tool for professional or other highly skilled translators is for the most part currently limited to postediting arrangements in which the translator invokes MT when desired and then manually cleans up the results. A theoretically promising but hitherto largely unsuccessful alternative to postediting for this application is interactive machine translation (IMT), in which the translator and MT system work in tandem. We argue that past failures to make IMT viable as a tool for skilled translators have been the result of an infelicitous mode of interaction rather than any inherent flaw in the idea. As a solution, we propose a new style of IMT in which the target text under construction serves as the medium of communication between an MT system and its user. We describe the design, implementation, and performance of an automatic word completion system for translators which is intended to demonstrate the feasibility of the proposed approach, albeit in a very rudimentary form. Neuroimagery must be visually checked for unacceptable levels of distortion prior to processing. However, inspection is time-consuming, unreliable for detecting subtle distortions and often subjective. With the increasing volume of neuroimagery, objective measures of quality are needed in order to automate screening. To address this need, we have assessed the effectiveness of no-reference image quality measures, which quantify quality inherent to a single image. A data set of 1001 magnetic resonance images (MRIs) recorded from 143 subjects was used for this evaluation. The MRI images were artificially distorted with two levels of either additive Gaussian noise or intensity nouniformity created from a linear model. A total of 239 different quality measures were defined from seven overall families and used to discriminate images for the type and level of distortion. Analysis of Variance identified two families of quality measure that were most effective: one based on Natural Scene Statistics and one originally developed to measure distortion caused by image compression. Measures from both families reliably discriminated among undistorted images, noisy images, and images distorted by intensity nonuniformity. The best quality measures were sensitive only to the distortion category and were not significantly affected by other factors. The results are encouraging enough that several quality measures are being incorporated in a real world MRI test bed. Abstract The huge amount of degraded documents stored in libraries and archives around the world needs automatic procedures of enhancement, classification, transliteration, etc. While high-quality images of these documents are in general easy to be captured, the amount of damage these documents contain before imaging is unknown. It is highly desirable to measure the severity of degradation that each document image contains. The degradation assessment can be used in tuning parameters of processing algorithms, selecting the proper algorithm, finding damaged or exceptional documents, among other applications. In this paper, the first dataset of degraded document images along with the human opinion scores for each document image is introduced in order to evaluate the image quality assessment metrics on historical document images. In this research, human judgments on the overall quality of the document image are used instead of the previously used OCR performance. Also, we propose an objective no reference quality metric based on the statistics of the mean subtracted contrast normalized (MSCN) coefficients computed from segmented layers of each document image. The segmentation into four layers of foreground and background is done on the basis of an analysis of the log-Gabor filters. This segmentation is based on the assumption that the sensitivity of the human visual system (HVS) is different at the locations of text and non-text. Experimental results show that the proposed metric has comparable or better performance than the state-of-the-art metrics, while it has a moderate complexity. The developed dataset as well as the Matlab source code of the proposed metric is available at http: www.synchromedia.ca system files VDIQA.zip . Abstract Image Quality Assessment (IQA) plays an important role in assessing any new hardware, software, image acquisition techniques, image reconstruction or post-processing algorithms, etc. In the past decade, there have been various IQA methods designed to evaluate natural images. Some were used for the medical images but the use was limited. This paper reviews the recent advancement on IQA for medical images, mainly for Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and ultrasonic imaging. Thus far, there is no gold standard of IQA for medical images due to various difficulties in designing a suitable IQA for medical images, and there are many different image characteristics and contents across various imaging modalities. No reference-IQA (NR-IQA) is recommended for assessing medical images because there is no perfect reference image in the real world medical imaging. We will discuss and comment on some useful and interesting IQA methods, and then suggest several important factors to be considered in designing a new IQA method for medical images. There is still great potential for research in this area. MRI has evolved into an important diagnostic technique in medical imaging. However, reliability of the derived diagnosis can be degraded by artifacts, which challenge both radiologists and automatic computer-aided diagnosis. This paper proposes a fully automatic method for measuring image quality of 3D structural MRI. Quality measures are derived by analyzing the air background of magnitude images and are capable of detecting image degradation from several sources, including bulk motion, residual magnetization from incomplete spoiling, blurring, ghosting, etc. The method has been validated on 749 3D T1-weighted 1.5 T and 3 T head scans acquired at 36 Alzheimer's Disease Neuroimaging Initiative (ADNI) study sites operating with various software and hardware combinations. Results are compared against qualitative grades assigned by the ADNI quality control center (taken as the reference standard). The derived quality indices are independent of the MRI system used and agree with the reference standard quality ratings with high sensitivity and specificity (>85 ). The proposed procedures for quality assessment could be of great value for both research and routine clinical imaging. It could greatly improve workflow through its ability to rule-out the need for a repeat scan while the patient is still in the magnet bore. The author investigated automatic extraction of left ventricular contours from cardiac magnetic resonance imaging (MRI) studies. The contour extraction algorithms were based on active contour models, or snakes. Based on cardiac MR image characteristics, the author suggested algorithms for extracting contours from these large data sets. The author specifically considered contour propagation methods to make the contours reliable enough despite noise, artifacts, and poor temporal resolution. The emphasis was on reliable contour extraction with a minimum of user interaction. Both spin echo and gradient echo studies were considered. The extracted contours were used for determining quantitative measures for the heart and could also be used for obtaining graphically rendered cardiac surfaces. > Detecting blood vessels in retinal images with the presence of bright and dark lesions is a challenging unsolved problem. In this paper, a novel multiconcavity modeling approach is proposed to handle both healthy and unhealthy retinas simultaneously. The differentiable concavity measure is proposed to handle bright lesions in a perceptive space. The line-shape concavity measure is proposed to remove dark lesions which have an intensity structure different from the line-shaped vessels in a retina. The locally normalized concavity measure is designed to deal with unevenly distributed noise due to the spherical intensity variation in a retinal image. These concavity measures are combined together according to their statistical distributions to detect vessels in general retinal images. Very encouraging experimental results demonstrate that the proposed method consistently yields the best performance over existing state-of-the-art methods on the abnormal retinas and its accuracy outperforms the human observer, which has not been achieved by any of the state-of-the-art benchmark methods. Most importantly, unlike existing methods, the proposed method shows very attractive performances not only on healthy retinas but also on a mixture of healthy and pathological retinas. Objective image video quality metrics which accurately represent the subjective quality of processed images are of paramount importance for the design and assessment of an image compression and transmission system. In some scenarios, it is also important to evaluate the quality of the received image with minimal reference to the transmitted one. For instance, for closed-loop optimization of a transmission system, the image quality measure can be evaluated at the receiver and provided as feedback information to the system controller. The original image - prior to compression and transmission - is not usually available at the receiver side, and it is important to rely at the receiver side on an objective quality metric that does not need reference or needs minimal reference to the original image. The observation that the human eye is very sensitive to edge and contour information of an image underpins the proposal of our reduced reference (RR) quality metric, which compares edge information between the distorted and the original image. Results highlight that the metric correlates well with subjective observations, also in comparison with commonly used full-reference metrics and with a state-of-the-art reduced reference metric. Automatically learned quality assessment for images has recently become a hot topic due to its usefulness in a wide variety of applications, such as evaluating image capture pipelines, storage techniques, and sharing media. Despite the subjective nature of this problem, most existing methods only predict the mean opinion score provided by data sets, such as AVA and TID2013. Our approach differs from others in that we predict the distribution of human opinion scores using a convolutional neural network. Our architecture also has the advantage of being significantly simpler than other methods with comparable performance. Our proposed approach relies on the success (and retraining) of proven, state-of-the-art deep object recognition networks. Our resulting network can be used to not only score images reliably and with high correlation to human perception, but also to assist with adaptation and optimization of photo editing enhancement algorithms in a photographic pipeline. All this is done without need for a “golden” reference image, consequently allowing for single-image, semantic- and perceptually-aware, no-reference quality assessment. Abstract Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor proliferation assessment by image analysis only focused on mitosis detection in predefined tumor regions. However, in a real-world scenario, automatic mitosis detection should be performed in whole-slide images (WSIs) and an automatic method should be able to produce a tumor proliferation score given a WSI as input. To address this, we organized the TUmor Proliferation Assessment Challenge 2016 (TUPAC16) on prediction of tumor proliferation scores from WSIs. The challenge dataset consisted of 500 training and 321 testing breast cancer histopathology WSIs. In order to ensure fair and independent evaluation, only the ground truth for the training dataset was provided to the challenge participants. The first task of the challenge was to predict mitotic scores, i.e., to reproduce the manual method of assessing tumor proliferation by a pathologist. The second task was to predict the gene expression based PAM50 proliferation scores from the WSI. The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of κ = 0.567, 95 CI [0.464, 0.671] between the predicted scores and the ground truth. For the second task, the predictions of the top method had a Spearman's correlation coefficient of r = 0.617, 95 CI [0.581 0.651] with the ground truth. This was the first comparison study that investigated tumor proliferation assessment from WSIs. The achieved results are promising given the difficulty of the tasks and weakly-labeled nature of the ground truth. However, further research is needed to improve the practical utility of image analysis methods for this task.