The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Exploring the spatial interactions between tumor and the inflammatory microenvironment using digital pathology image analysis can contribute to a better understanding of the immune function and tumor heterogeneity. We address this by providing tools able to reveal various metrics describing spatial relationships in the cancer ecosystem. The approach comprises nuclei segmentation and classification,...
Automatic MR whole prostate segmentation is a challenging task. Recent approaches have attempted to harness the capabilities of deep learning for MR prostate segmentation to tackle pixel-level labeling tasks. Patch-based and hierarchical features-based deep CNN models were used to delineate the prostate boundary. To further investigate this problem, we introduce a Holistically-Nested Edge Detector...
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,...
Segmentation of skin lesions is considered as an important step in computer aided diagnosis (CAD) for melanoma diagnosis. There have many attempts to segment skin lesions in a semi- or fully-automated manner. Existing methods, however, have problems with over- or under-segmentation and do not perform well with challenging skin lesions such as when a lesion is partially connected to the background...
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...
In this paper, we propose an end-to-end trainable Convolutional Neural Network (CNN) architecture called the M-net, for segmenting deep (human) brain structures from Magnetic Resonance Images (MRI). A novel scheme is used to learn to combine and represent 3D context information of a given slice in a 2D slice. Consequently, the M-net utilizes only 2D convolution though it operates on 3D data, which...
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...
In this paper we address the problem of bone segmentation in MRI images of children, in the region of the pelvis. To cope with the complex structure of the bones in this region and their changing topology during growth, we propose a method relying on 3D bone templates. These models are built from 3D CT images. For a given MRI volume, the closest template is chosen and registered on the MRI data. This...
We present an interactive approach to train a deep neural network pixel classifier for the segmentation of neuronal structures. An interactive training scheme reduces the extremely tedious manual annotation task that is typically required for deep networks to perform well on image segmentation problems. Our proposed method employs a feedback loop that captures sparse annotations using a graphical...
The fovea is one of the most important anatomical landmarks in the eye and its localization is required in automated analysis of retinal diseases due to its role in sharp central vision. In this paper, we propose a two-stage deep learning framework for accurate segmentation of the fovea in retinal colour fundus images. In the first stage, coarse segmentation is performed to localize the fovea in the...
Accurate segmentation of hip joint cartilage from magnetic resonance (MR) images provides a basis for obtaining morphometric data of articular cartilages for investigation of pathoanatomical conditions such as osteoarthritis. In this paper, we present an automated MR-based cartilage segmentation method using an ensemble of neural networks for the individual femoral and acetabular cartilage plates...
The automated detection of cerebral vessels is of great importance in understanding of the diagnosis, treatment and mechanism of many brain vascular pathologies. However, automatic vessel detection from 3D angiography continues to be an open issue. In this paper we introduce a novel 3D symmetry filter that has excellent performance on enhancing vessels in magnetic resonance angiography (MRA). The...
We propose an automated framework for lung nodule segmentation from pulmonary CT scan using graph cut with a deep learned prior. The segmentation problem is formulated as a hybrid cost function minimization task, which combines a domain specific data term with a deep learned probability map. The proposed segmentation framework embodies the robustness of deep learning in object localization, while...
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...
For electrophysiology procedures, obtaining the information of scar within the left ventricle is very important for diagnosis, therapy planning and patient prognosis. The clinical gold standard to visualize scar is late-gadolinium-enhanced-MRI (LGE-MRI). The viability assessment of the myocardium often requires the prior segmentation of the left ventricle (LV). To overcome this problem, we propose...
Segmentation of the developing cortical plate from MRI data of the post-mortem fetal brain is highly challenging due to partial volume effects, low contrast, and heterogeneous maturation caused by ongoing myelination processes. We present a new atlas-free method that segments the inner and outer boundaries of the cortical plate in fetal brains by exploiting diffusion-weighted imaging cues and using...
The segmentation of media-adventitia and lumen-intima boundaries of the Carotid Artery forms an essential part in assessing plaque morphology in Ultrasound Imaging. Manual methods are tedious and prone to variability and thus, developing automated segmentation algorithms is preferable. In this paper, we propose to use deep convolutional networks for automated segmentation of the media-adventitia boundary...
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...
We propose a novel label fusion technique as well as a crowdsourcing protocol to efficiently obtain accurate epithelial cell segmentations from non-expert crowd workers. Our label fusion technique simultaneously estimates the true segmentation, the performance levels of individual crowd workers, and an image segmentation model in the form of a pairwise Markov random field. We term our approach image-aware...
Artery-vein classification on pulmonary computed tomography (CT) images is becoming of high interest in the scientific community due to the prevalence of pulmonary vascular disease that affects arteries and veins through different mechanisms. In this work, we present a novel approach to automatically segment and classify vessels from chest CT images. We use a scale-space particle segmentation to isolate...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.