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Breast mass detection and segmentation are difficult tasks due to the variation in size and shape of breast masses. Constructing classifiers for this problem is also challenging due to the fact that normal tissue regions overwhelmingly outnumber abnormal regions. In this paper, we propose a novel approach for detecting and segmenting breast masses in mammography based on multi-scale morphological...
In this paper, we propose new prognostic methods that predict 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and segmentation maps of multiple anatomic structures. We explore two approaches: 1) a unified framework based on two state-of-the-art deep learning...
Age-related macular degeneration (AMD) is a major cause of irreversible blindness and loss of vision in people over 50 years old. Fluid (or cyst) regions such as intraretinal fluid (IRF), subretinal fluid (SRF), and sub-retinal pigment epithelium (sub-RPE), have different tissue appearance in Optical Coherence Tomography (OCT) images compared to normal retina tissue and are a defining feature of AMD...
Positron Emission Tomography (PET) using 18F-FDG is recognized as the modality of choice for lymphoma, due to its high sensitivity and specificity. Its wider use for the detection of lesions, quantification of their metabolic activity and evaluation of response to treatment demands the development of accurate and reproducible quantitative image interpretation tools. An accurate tumour delineation...
Automatic segmentation of retinal blood vessels from fundus images plays an important role in the computer aided diagnosis of retinal diseases. The task of blood vessel segmentation is challenging due to the extreme variations in morphology of the vessels against noisy background. In this paper, we formulate the segmentation task as a multi-label inference task and utilize the implicit advantages...
Detection of calcified plaques in coronary arteries is helpful in cardiovascular disease risk assessment. This is often performed by radiologists on computed tomography (CT) images. We work towards an automatic solution for calcium detection in CT images. Most of previous work in this area combines CT and CTA for this purpose to facilitate the localization of the coronary arteries. Given the cost...
Skin melanoma is one of the highly addressed health problems in many countries. Dermatologists diagnose melanoma by visual inspections of mole using clinical assessment tools such as ABCD. However, computer vision tools have been introduced to assist in quantitative analysis of skin lesions. Deep learning is one of the trending machine learning techniques that have been successfully utilized to solve...
We consider the problem of domain shift in analyses of brain MRI data. While many different datasets are publicly available, most algorithms are still trained on a single dataset and often suffer the problem of limited and unbalanced sample sizes. In this work, we propose a surprisingly simple strategy to reduce the impact of domain shift - caused by different data sources and processing pipelines...
Detection and segmentation of small renal mass (SRM) in renal CT images are important pre-processing for computer-aided diagnosis of renal cancer. However, the task is known to be challenging due to its variety of size, shape, and location. In this paper, we propose an automated method for detecting and segmenting SRM in contrast-enhanced CT images using texture and context feature classification...
Biological membranes are one of the most basic structures and regions of interest in cell biology. In the study of membranes, segment extraction is a well-known and difficult problem because of impeding noise, directional and thickness variability, etc. Recent advances in electron microscopy membrane segmentation are able to cope with such difficulties by training convolutional neural networks. However,...
Computerized prenatal ultrasound (US) image segmentation methods can greatly improve the efficiency and objectiveness of the biometry interpretation. However, the boundary incompleteness and ambiguity in US images hinder the automatic solutions severely. In this paper, we propose a cascaded framework for fully automatic US image segmentation. A customized Fully Convolutional Network (FCN) was utilized...
Automatic non-invasive assessment of hepatocellular carcinoma (HCC) malignancy has the potential to substantially enhance tumor treatment strategies for HCC patients. In this work we present a novel framework to automatically characterize the malignancy of HCC lesions from DWI images. We predict HCC malignancy in two steps: As a first step we automatically segment HCC tumor lesions using cascaded...
Precise and objective segmentation of atrial scarring (SAS) is a prerequisite for quantitative assessment of atrial fibrillation using non-invasive late gadolinium-enhanced (LGE) MRI. This also requires accurate delineation of the left atrium (LA) and pulmonary veins (PVs) geometry. Most previous studies have relied on manual segmentation of LA wall and PVs, which is a tedious and error-prone procedure...
Developmental dysplasia of the hip (DDH) is a congenital deformity occurring in ∼3% of infants. If diagnosed early most cases of DDH can be effectively treated using a Pavlik harness. However, current diagnosis of DDH using 2D ultrasound is and can have high inter-operator variability. In this paper we propose a method to automatically segment the acetabulum bone and derive geometric indices of hip...
A significant amount of research has been done on the segmentation of coronary arteries. However, the resulting automated boundary delineation is still not suitable for clinical utilization. The convolutional neural network was driving advances in the medical image processing. We propose the brief convolutional network (BCN) that automatically produces the labeled mask with the luminal and wall boundaries...
Detection of infarcted myocardium in the left ventricle is achieved with delayed enhancement magnetic resonance imaging (DE-MRI). However, manual segmentation is tedious and prone to variability. We studied three texture analysis methods (run-length matrix, co-occurrence matrix, and autoregressive model) in combination with histogram features to characterize the infarcted myocardium. We evaluated...
This paper describes an artificial neural network (ANN) method that employs a feature-learning algorithm to detect the lumen and MA borders in intravascular ultrasound (IVUS) images. Three types of imaging features including spatial, neighboring, and gradient features were used as the input features to the neural network, and then the different vascular layers were distinguished using two sparse autoencoders...
While accurate tumor delineation in FDG-PET is a vital task, noisy and blurring imaging system makes it a challenging work. In this paper, we propose to address this issue using the theory of belief functions, a powerful tool for modeling and reasoning with uncertain and/or imprecise information. An automatic segmentation method based on clustering is developed in 3-D, where, different from available...
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