Deep learning (DL) emerged as a powerful tool for object detection and classification in medical images. Building a well-performing DL model, however, requires a huge number of images for training, and it takes days to train a DL model even on a cutting edge high-performance computing platform. This study is aimed at developing a method for selecting a “small” number of representative samples from a large collection of training samples to train a DL model for the could be used to detect polyps in CT colonography (CTC), without compromising the classification performance. Our proposed method for representative sample selection (RSS) consists of a K-means clustering algorithm. For the performance evaluation, we applied the proposed method to select samples for the training of a massive training artificial neural network based DL model, to be used for the classification of polyps and non-polyps in CTC. Our results show that the proposed method reduce the training time by a factor of 15, while maintaining the classification performance equivalent to the model trained using the full training set. We compare the performance using area under the receiveroperating- characteristic curve (AUC).
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.