Colon cancer is the second cancer killer in the US [1]. Colonoscopy is the primary method for screening and prevention of colon cancer, but during colonoscopy, a significant number (25% [2]) of polyps (precancerous abnormal growths inside of the colon) are missed; therefore, the goal of our research is to reduce the polyp miss-rate of colonoscopy. This paper presents a method to detect polyp automatically in a colonoscopy video. Our system has two stages: Candidate generation and candidate classification. In candidate generation (stage 1), we chose 3,463 frames (including 1,718 with-polyp frames) from real-time colonoscopy video database. We first applied processing procedures, namely intensity adjustment, edge detection and morphology operations, as pre-preparation. We extracted each connected component (edge contour) as one candidate patch from the pre-processed image. With the help of ground truth (GT) images, 2 constraints were implemented on each candidate patch, dividing and saving them into polyp group and non-polyp group. In candidate classification (stage 2), we trained and tested convolutional neural networks (CNNs) with AlexNet architecture [3] to classify each candidate into with-polyp or non-polyp class. Each with-polyp patch was processed by rotation, translation and scaling for invariant to get a much robust CNNs system. We applied leave-2-patients-out cross-validation on this model (4 of 6 cases were chosen as training set and the rest 2 were as testing set). The system accuracy and sensitivity are 91.47% and 91.76%, respectively.
KEYWORDS: Ultrasonography, Video, Arteries, Electrocardiography, Software development, Simulation of CCA and DLA aggregates, Signal detection, Image segmentation, Autoregressive models, Chemical vapor deposition
Carotid intima-media thickness (CIMT) has proven to be sensitive for predicting individual risk of cardiovascular diseases (CVD). The CIMT is measured based on region of interest (ROIs) in end-diastolic ultrasound frames (EUFs). To interpret CIMT videos, in the current practice, the EUFs and ROIs must be manually selected, a process that is tedious and time consuming. To reduce CIMT interpretation time, this paper presents a novel method for automatically selecting EUFs and determining ROIs in ultrasound videos. The EUFs are selected based on the QRS complex of the electrocardiogram (ECG) signal associated with the ultrasound video, and the ROI is detected based on image intensity and curvature of the carotid artery bulb. Once a EUF is selected and its corresponding ROI is determined, our system measures CIMT using the snake algorithm extended with hard constraints [1,6-7] by computing the average thickness and maximum thickness, calculating the vascular age, and generating a patient’s report. In this study, we utilize 23 subjects. Each subject has 4 videos, and 3 EUFs are selected in each video, resulting in a total of 272 ROIs. By comparing with the reference provided by an expert for both frame selection and ROI detection, we achieve 92.96% sensitivity and 97.62% specificity for EUF selection, and 81.25% accuracy in ROI detection.
Acute pulmonary embolism (APE) is known as one of the major causes of sudden death. However, high level of mortality
caused by APE can be reduced, if detected in early stages of development. Hence, biomarkers capable of early detection
of APE are of utmost importance. This study investigates how APE affects the biomechanics of the cardiac right ventricle
(RV), taking one step towards developing functional biomarkers for early diagnosis and determination of prognosis of APE.
To that end, we conducted a pilot study in pigs, which revealed the following major changes due to the severe RV afterload
caused by APE: (1) waving paradoxical motion of the RV inner boundary, (2) decrease in local curvature of the septum,
(3) lower positive correlation between the movement of inner boundaries of the septal and free walls of the RV, (4) slower
blood ejection by the RV, and (5) discontinuous movement observed particularly in the middle of the RV septal wall.
KEYWORDS: Angiography, Medical imaging, Computer aided diagnosis and therapy, Feature extraction, Solids, Health informatics, Detection and tracking algorithms, Machine learning, Pattern recognition, 3D image processing
In this paper, we propose a self-adaptive, asymmetric on-line boosting (SAAOB) method for detecting anatomical structures
in CT pulmonary angiography (CTPA). SAAOB is novel in that it exploits a new asymmetric loss criterion with
self-adaptability according to the ratio of exposed positive and negative samples and in that it has an advanced rule to
update sample's importance weight taking account of both classification result and sample's label. Our presented method
is evaluated by detecting three distinct thoracic structures, the carina, the pulmonary trunk and the aortic arch, in both
balanced and imbalanced conditions.
Assessment of Carotid Intima-Media Thickness (CIMT) by B-mode ultrasound is a technically mature and reproducible
technology. Given the high morbidity, mortality and the large societal burden associated with CV diseases, as a safe
yet inexpensive tool, CIMT is increasingly utilized for cardiovascular (CV) risk stratification. However, CIMT
requires a precise measure of the thickness of the intima and media layers of the carotid artery that can be tedious, time
consuming, and demand specialized expertise and experience. To this end, we have developed a highly user-friendly
system for semiautomatic CIMT image interpretation. Our contribution is the application of active contour models
(snake models) with hard constraints, leading to an accurate, adaptive and user-friendly border detection algorithm. A
comparison study with the CIMT measurement software in Siemens Syngo® Arterial Health Package shows that our
system gives a small bias in mean (0.049 ±0.051mm) and maximum (0.010 ± 0.083 mm) CIMT measures and offers a
higher reproducibility (average correlation coefficients were 0.948 and 0.844 in mean and maximum CIMT respectively
(P <0.001)). This superior performance is attributed to our novel interface design for hard constraints in the snake
models.
Pulmonary embolism is a common cardiovascular emergency with about 600,000 cases occurring annually and causing
approximately 200,000 deaths in the US. CT pulmonary angiography (CTPA) has become the reference standard for PE
diagnosis, but the interpretation of these large image datasets is made complex and time consuming by the intricate
branching structure of the pulmonary vessels, a myriad of artifacts that may obscure or mimic PEs, and suboptimal bolus
of contrast and inhomogeneities with the pulmonary arterial blood pool. To meet this challenge, several approaches for
computer aided diagnosis of PE in CTPA have been proposed. However, none of these approaches is capable of
detecting central PEs, distinguishing the pulmonary artery from the vein to effectively remove any false positives from
the veins, and dynamically adapting to suboptimal contrast conditions associated the CTPA scans. To overcome these
shortcomings, it requires highly efficient and accurate identification of the pulmonary trunk. For this very purpose, in
this paper, we present a machine learning based approach for automatically detecting the pulmonary trunk. Our idea is to
train a cascaded AdaBoost classifier with a large number of Haar features extracted from CTPA image samples, so that
the pulmonary trunk can be automatically identified by sequentially scanning the CTPA images and classifying each
encountered sub-image with the trained classifier. Our approach outperforms an existing anatomy-based approach,
requiring no explicit representation of anatomical knowledge and achieving a nearly 100% accuracy tested on a large
number of cases.
Electronic colon cleansing (ECC) aims to remove the contrast agent from the CT abdominal images so that a virtual
model of the colon can be constructed. Virtual colonoscopy requires either liquid or solid preparation of the colon before
CT imaging. This paper has two parts to address ECC in both preparation methods. In the first part, meniscus removal in
the liquid preparation is studied. The meniscus is the curve seen at the top of a liquid in response to its container. Left on
the colon wall, the meniscus can decrease the sensitivity and specificity of virtual colonoscopy. We state the differential
equation that governs the profile of the meniscus and propose an algorithm for calculating the boundary of the contrast
agent. We compute the surface tension of the liquid-colon wall contact using in-vivo CT data. Our results show that the
surface tension can be estimated with an acceptable degree of uncertainty. Such an estimate, along with the meniscus
profile differential equation will be used as an a priori knowledge to aid meniscus segmentation. In the second part, we
study ECC in solid preparation of colon. Since the colon is pressurized with air before acquisition of the CT images, a
prior on the shape of the colon wall can be obtained. We present such prior and investigate it using patient data. We
show the shape prior is held in certain parts of the colon and propose a method that uses this prior to ease pseudoenhancement
correction.
Pulmonary embolism (PE) is a serious medical condition, characterized by the partial/complete blockage of an
artery within the lungs. We have previously developed a fast yet effective approach for computer aided detection
of PE in computed topographic pulmonary angiography (CTPA),1 which is capable of detecting both acute and
chronic PEs, achieving a benchmark performance of 78% sensitivity at 4 false positives (FPs) per volume. By
reviewing the FPs generated by this system, we found the most dominant type of FP, roughly one third of all
FPs, to be lymph/connective tissue. In this paper, we propose a novel approach that specifically aims at reducing
this FP type. Our idea is to explicitly exploit the anatomical context configuration of PE and lymph tissue in the
lungs: a lymph FP connects to the airway and is located outside the artery, while a true PE should not connect
to the airway and must be inside the artery. To realize this idea, given a detected candidate (i.e. a cluster of
suspicious voxels), we compute a set of contextual features, including its distance to the airway based on local
distance transform and its relative position to the artery based on fast tensor voting and Hessian "vesselness"
scores. Our tests on unseen cases show that these features can reduce the lymph FPs by 59%, while improving
the overall sensitivity by 3.4%.
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