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

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

Fuzzy Connectedness Image Co-segmentation for HybridPET/MRI and PET/CT Scans

In this paper, we presented a 3-D computer-aided co-segmentation tool for tumor/lesion detection and quantification from hybrid PET/MRI and PET/CT scans. The proposed method was designed with a novel modality-specific visibility weighting scheme built upon a fuzzy connectedness (FC) image segmentation algorithm. In order to improve the determination of lesion margin, it is necessary to combine the complementary information of tissues from both anatomical and functional domains. Therefore, a robust image segmentation method that simultaneously segments tumors/lesions in each domain is required. However, this task, named cosegmentation, is a challenging problem due to (1) unique challenges brought by each imaging modality, and (2) a lack of one-to-one region and boundary correspondences of lesions in different imaging modalities. Owing to these hurdles, the algorithm is desired to have a sufficient flexibility to utilize the strength of each modality. In this work, seed points were first selected from high uptake regions within PET images. Then, lesion boundaries were delineated using a hybrid approach based on novel affinity function design within the FC framework. Further, an advanced extension of FC algorithm called iterative relative FC (IRFC) was used with automatically identified background seeds. The segmentation results were compared to the reference truths provided by radiologists. Experimental results showed that the proposed method effectively utilized multi-modality information for co-segmentation, with a high accuracy (mean DSC of 85%) and can be a viable alternative to the state-of-the art joint segmentation method of random walk (RW) with higher efficiency