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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...
In the field of pathological image analysis based on machine learning, the generation of appropriate training data set is significant but difficult. As a solution, this paper addresses a novel unsupervised region proposal method for histopathological whole slide image based on Selective Search. Specifically, the method utilizes multiple magnifications, modifies the similarity measure for grouping...
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...
Wireless Capsule Endoscopy (WCE) is a novel diagnostic modality of endoscopic imaging which facilitates direct visualization of the gastrointestinal (GI) tract. Many computational methods that can automatically detect and/or characterize the abnormalities from WCE sequences are developed to support medical decision-making. This paper presents a new approach for automated segmentation of blood regions...
Automated segmentation of brain structures from MR images is an important practice in many neuroimage studies. In this paper, we explore the utilization of a multi-view ensemble approach that relies on neural networks (NN) to combine multiple decision maps in achieving accurate hippocampus segmentation. Constructed under a general convolutional NN structure, our Ensemble-Net networks explore different...
Deformable registration based multi-atlas segmentation has been successfully applied in a broad range of anatomy segmentation applications. However, the excellent performance comes with a high computational burden due to the requirement for deformable image registration and voxel-wise label fusion. To address this problem, we conduct an experimental study to investigate trade-off between computational...
Streaking artifacts caused by metallic objects severely affect the visual quality of CT images, resulting in medical misdiagnosis. Commonly used approaches for metal artifact reduction usually consist of interpolation and iterative methods. The former one tends to lose image quality by introducing extra artifacts, while the latter is more computational expensive. This paper proposes a new approach...
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...
Reflectance confocal microscopy (RCM) is a powerful tool to visualize the skin layers at cellular resolution. The dermal-epidermal junction (DEJ) is a thin complex 3D structure. It appears as a low-contrasted structure in confocal en-face sections, which is difficult to recognize visually, leading to uncertainty in the classification. In this article, we propose an automated method for segmenting...
Accurate skin lesion segmentation is an important yet challenging problem for medical image analysis. The skin lesion segmentation is subject to variety of challenges such as the significant pattern and colour diversity found within the lesions, presence of various artifacts, etc. In this paper, we present two fully convolutional networks with several side outputs to take advantage of discriminative...
Medical image segmentation plays an important role in digital medical research, therapy planning, and computer aided diagnosis. However, the existence of noise and low contrast make automatic liver segmentation remains an open challenge. In this work we focus on a novel variational semi-automatic liver segmentation method. First, we used the signed distance functions (SDF) representing pattern shapes...
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...
Placental volume measured with 3D ultrasound in the first trimester has been shown to be correlated to adverse pregnancy outcomes. This could potentially be used as a screening test to predict the “at risk” pregnancy. However, manual segmentation whilst previously shown to be accurate and repeatable is very time consuming and semi-automated methods still require operator input. To generate a screening...
Endometrium assessment via thickness measurement is commonly performed in routine gynecological ultrasound examination for assessing the reproductive health of patients undergoing fertility related treatments and endometrium cancer screening in women with post-menopausal bleeding. This paper introduces a fully automated technique for endometrium thickness measurement from three-dimensional transvaginal...
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 introduce a new fully automated breast mass segmentation method from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The method is based on globally optimal inference in a continuous space (GOCS) using a shape prior computed from a semantic segmentation produced by a deep learning (DL) model. We propose this approach because the limited amount of annotated training samples does...
In this paper, we propose a multi-view deep residual neural network (mResNet) for the fully automated classification of mammograms as either malignant or normal/benign. Specifically, our mResNet approach consists of an ensemble of deep residual networks (ResNet), which have six input images, including the unregistered craniocaudal (CC) and mediolateral oblique (MLO) mammogram views as well as the...
We present a learning based fully automatic method to detect and segment the prostate in T2 weighted MR scans. It consists of a localization stage which uses a learned global context to detect the prostate location. This is followed by a segmentation stage which uses a learned local context using prostatic segment specific discriminative classifiers, to compute the probability of a point being on...
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