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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...
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...
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...
We present a new surface energy potential for the segmentation of cylindrical objects in 3D medical imaging using parametric spline active contours (a.k.a. spline-snakes). Our energy formulation is based on an optimal steerable surface detector. Thus, we combine the concept of steerability with spline-snakes that have open topology for semi-automatic segmentation. We show that the proposed energy...
With the advancement of high throughput and high resolution volumetric brain imaging, there is an unmet need to trace dense neuron fibers and study long-range neuron connectivity. An initial pipeline is described for processing cellular-level neuronal fiber data acquired by a new super resolution imaging method called Magnified Analysis of the Proteome (MAP). First, a multiscale vessel enhancement...
The evolutionary success of ants and other social insects is considered to be intrinsically linked to division of labor and emergent collective intelligence. The role of the brains of individual ants in generating these processes, however, is poorly understood. One genus of ant of special interest is Pheidole, which includes more than a thousand species, most of which are dimorphic, i.e. their colonies...
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...
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...
Image segmentation is an important step in the quantitative analysis of fluorescence microscopy data. Since fluorescence microscopy volumes suffer from intensity inhomogeneity, low image contrast and limited depth resolution, poor edge details, and irregular structure shape, segmentation still remains a challenging problem. This paper describes a nuclei segmentation method for fluorescence microscopy...
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...
In recent years, magnetic resonance imaging (MRI) has been explored for non-invasive assessment of renal transplant function. This paper proposes a computer-aided diagnostic (CAD) system for the assessment of renal transplant status, which integrates both clinical and MRI-derived biomarkers. The latter are derived from either 3D (2D + time) dynamic contrast-enhanced MRI or 4D (3D + b-value) diffusion-weighted...
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...
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...
Biplane X-ray angiography is currently the gold standard for navigational guidance during percutaneous interventions in vascular structures; but it remains limited to 2D projections. In this study, we propose a novel graph-based voxel coloring method for 3D reconstruction of vascular structures from biplane X-ray angiography sequences. The reconstruction is obtained by using the random walks algorithm...
The relation between normal and pathological aging and the cerebrovascular component is still unclear. In this context, the common marmoset, which has the advantage of enabling longitudinal studies over a reasonable timeframe, appears as a good pre-clinical model. However, there is still a lack of quantitative information on the macrovascular structure of the marmoset brain. In this paper, we investigate...
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