77 research outputs found
CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans
Accurate and fast extraction of lung volumes from computed tomography (CT)
scans remains in a great demand in the clinical environment because the
available methods fail to provide a generic solution due to wide anatomical
variations of lungs and existence of pathologies. Manual annotation, current
gold standard, is time consuming and often subject to human bias. On the other
hand, current state-of-the-art fully automated lung segmentation methods fail
to make their way into the clinical practice due to their inability to
efficiently incorporate human input for handling misclassifications and praxis.
This paper presents a lung annotation tool for CT images that is interactive,
efficient, and robust. The proposed annotation tool produces an "as accurate as
possible" initial annotation based on the fuzzy-connectedness image
segmentation, followed by efficient manual fixation of the initial extraction
if deemed necessary by the practitioner. To provide maximum flexibility to the
users, our annotation tool is supported in three major operating systems
(Windows, Linux, and the Mac OS X). The quantitative results comparing our free
software with commercially available lung segmentation tools show higher degree
of consistency and precision of our software with a considerable potential to
enhance the performance of routine clinical tasks.Comment: 4 pages, 6 figures; to appear in the proceedings of 36th Annual
International Conference of the IEEE Engineering in Medicine and Biology
Society (EMBC 2014
3B11-N, a monoclonal antibody against MERS-CoV, reduces lung pathology in rhesus monkeys following intratracheal inoculation of MERS-CoV Jordan-n3/2012
Middle East Respiratory Syndrome Coronavirus (MERS-CoV) was identified in 2012 as the causative agent of a severe, lethal respiratory disease occurring across several countries in the Middle East. To date there have been over 1,600 laboratory confirmed cases of MERS-CoV in 26 countries with a case fatality rate of 36%. Given the endemic region, it is possible that MERS-CoV could spread during the annual Hajj pilgrimage, necessitating countermeasure development. In this report, we describe the clinical and radiographic changes of rhesus monkeys following infection with 5×106 PFU MERS-CoV Jordan-n3/2012. Two groups of NHPs were treated with either a human anti-MERS monoclonal antibody 3B11-N or E410-N, an anti-HIV antibody. MERS-CoV Jordan-n3/2012 infection resulted in quantifiable changes by computed tomography, but limited other clinical signs of disease. 3B11-N treated subjects developed significantly reduced lung pathology when compared to infected, untreated subjects, indicating that this antibody may be a suitable MERS-CoV treatment
Near-optimal keypoint sampling for fast pathological lung segmentation
Abstract — Accurate delineation of pathological lungs from computed tomography (CT) images remains mostly unsolved because available methods fail to provide a reliable generic solution due to high variability of abnormality appearance. Local descriptor-based classification methods have shown to work well in annotating pathologies; however, these methods are usually computationally intensive which restricts their wide-spread use in real-time or near-real-time clinical applications. In this paper, we present a novel approach for fast, accurate, reliable segmentation of pathological lungs from CT scans by combining region-based segmentation method with local-descriptor classification that is performed on an optimized sam-pling grid. Our method works in two stages; during stage one, we adapted the fuzzy connectedness (FC) image segmentation algorithm to perform initial lung parenchyma extraction. In the second stage, texture-based local descriptors are utilized to segment abnormal imaging patterns using a near optimal keypoint analysis by employing centroid of supervoxel as grid points. The quantitative results show that our pathological lung segmentation method is fast, robust, and improves on current standards and has potential to enhance the performance of routine clinical tasks. I
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Stem-cell therapy for renal diseases
Significant attention is currently directed to the biological and therapeutic capabilities of stem cells for developing novel treatments for acute and chronic kidney diseases. To date, viable sources of stem cells for renal therapies include adult bone marrow and embryonic tissues, including the metanephric mesenchyme and mesonephros. Native adult kidney stem cells have yet to be identified. Systemically introduced stem cells can engraft in sites of renal disease and injury to show donor phenotypes. Stem cells can differentiate into cells similar to glomeruli, mesangium, and tubules in the kidneys. The impact of stem-cell engraftment and differentiation on renal function presently is unknown. Identification of renal diseases treatable with stem-cell therapies is expected to evolve as stem-cell technologies advance. Methods of modifying stem cells to improve homing, differentiation, and integration into host tissues need further characterization. Ethical and legal controversies about embryonic research and cloning are shaping the regulation and funding of stem-cell research for kidney diseases. Scientific and clinical understanding of stem cells and their potential for renal treatments are in the early stage of development. This field offers great promise, and there are significant opportunities for future investigation in clinical, biological, and ethical aspects of stem-cell therapy for kidney diseases
Highly Precise Partial Volume Correction For Pet Images: An Iterative Approach Via Shape Consistency
Positron emission tomography (PET) is capable of capturing the functional information. A major limitation for PET imaging is the low spatial resolution, leading to partial volume effects (PVE). PVE introduces significant bias to the image quantification, causing compromised measurement for uptake regions, especially smaller ones. For quantitative PET, accurate uptake values are critical for diagnostic evaluation and treatment planning. Therefore, a partial volume correction (PVC) technique is highly desirable in order to avoid size-dependent underestimation for true activities. In this paper, we present a new iterative PVC approach for PET images. The proposed method uses the state-of-the-art simultaneous delineation and noise removal algorithm to estimate the local uptake regions. The delineation is further utilized for weighted PVC with regard to a shape consistency measurement. The process is performed iteratively until delineation convergence. Qualitative and quantitative results demonstrate that the proposed framework successfully corrects the PVE and preserves local structures
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