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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,...
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...
One of the most important prognostic markers to assess proliferation activity of breast tumors is estimating the number of mitotic figures in H&E stained tissue. We propose the use of a recently published convolutional neural network architecture, Wide Residual Networks, for mitosis detection in breast histology images. The model is trained to classify each pixel of on an image using as context...
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...
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...
Cell detection in microscopy images is a common and challenging task. We propose a new approach for mitotic cell detection in histopathology images, which is based on a Deep Residual Network architecture combined with Hough voting. We propose a voting layer for neural networks and introduce a novel loss function. Our approach is learned from scratch using cell centroids and the original images. We...
We propose a new computer-aided detection system that uses 3D convolutional neural networks (CNN) for detecting lung nodules in low dose computed tomography. The system leverages both a priori knowledge about lung nodules and confounding anatomical structures and data-driven machine-learned features and classifier. Specifically, we generate nodule candidates using a local geometric-model-based filter...
Automatic analysis of fetal echocardiography screening images could aid in the identification of congenital heart diseases. The first step towards automatic fetal echocardiography analysis is locating the fetal heart in an image and identifying the viewing (imaging) plane. This is highly challenging since the fetal heart is small with relatively indistinct anatomical structural appearance. This is...
Automatic recognition of surgical workflow is an unresolved problem among the community of computer-assisted interventions. Among all the features used for surgical workflow recognition, one important feature is the presence of the surgical tools. Extracting this feature leads to the surgical tool presence detection problem to detect what tools are used at each time in surgery. This paper proposes...
Fluorescence microscopy has emerged as a powerful tool for studying cell biology because it enables the acquisition of 3D image volumes deeper into tissue and the imaging of complex subcellular structures. Quantitative analysis of these structures, which is needed to characterize the structure and constitution of tissue volumes, is facilitated by nuclei segmentation. However, manual segmentation is...
Quantitative imaging biomarkers identification has become a powerful tool for predictive diagnosis given increasingly available clinical imaging data. In parallel, molecular profiles have been well documented in non-small cell lung cancers (NSCLCs). However, there has been limited studies on leveraging the two major sources for improving lung cancer computer-aided diagnosis. In this paper, we investigate...
Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning. While the variation in the appearance of the nodules remains large, there is a need for a fast and robust computer aided system. In this work, we propose an end-to-end trainable multi-view deep Convolutional Neural Network (CNN) for nodule characterization...
Detection of Architectural distortion (AD) is important for ruling out possible pre-malignant lesions in breast, but due to its subtlety, it is often missed on the screening mammograms. In this work we suggest a novel AD detection method based on region proposal convolution neural nets (R-CNN). When the data is scarce, as typically the case in medical domain, R-CNN yields poor results. In this study,...
Obstructive sleep apnea (OSA) is a medical condition in which the airway is repetitively obstructed and resulting in sleep disruption. Previous research has shown that this condition may be the cause or the result of the craniofacial structure, and that specific facial features such as ‘face width’ or ‘eye width’ are correlated with the risk of OSA. In this study we developed two automatic image processing...
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...
Histopathology forms the gold standard for confirmed diagnosis of a suspicious hyperplasia being benign or malignant and for its sub-typing. While techniques like whole-slide imaging have enabled computer assisted analysis for exhaustive reporting of the tissue section, it has also given rise to the big-data deluge and the time complexity associated with processing GBs of image data acquired over...
Analysis and interpretation of stained tumor sections is one of the main tools in cancer diagnosis and prognosis, which is mainly carried out manually by pathologists. The avent of digital pathology provides us with the challenging opportunity to automatically analyze large amounts of these complex image data in order to draw biological conclusions from them and to study cellular and tissular phenotypes...
Action recognition systems have the potential to support clinicians, coaches and physical therapists in identifying important adopted movement patterns which could aid injury detection potential or inform rehabilitation strategies. Currently, motion capture systems, structured light pattern and time-of-flight sensors have utilization limitations that place constraints on their use outside of the laboratory...
Tissue segmentation is an important pre-requisite for efficient and accurate diagnostics in digital pathology. However, it is well known that whole-slide scanners can fail in detecting all tissue regions, for example due to the tissue type, or due to weak staining because their tissue detection algorithms are not robust enough. In this paper, we introduce two different convolutional neural network...
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