Determining candidate polyp morphology from CT colonography using a level-set method

In this paper we propose a level-set segmentation for polyp candidates in Computer Tomography Colongraphy (CTC). Correct classification of the candidate polyps into polyp and non-polyp is, in most cases, evaluated using shape features. Therefore, accurate recovery of the polyp candidate surface is important for correct classification. The method presented in this paper, evolves a curvature and gradient dependent boundary to recover the surface of the polyp candidate in a level-set framework. The curvature term is computed using a combination of the Mean curvature and the Gaussian curvature. The results of the algorithm were run through a classifier for two complete data-sets and returned 100% sensitivity for polyps greater than 5mm

Polyphosphate granule biogenesis is temporally and functionally tied to cell cycle exit during starvation in Pseudomonas aeruginosa

Polyphosphate (polyP) granule biogenesis is an ancient and ubiquitous starvation response in bacteria. Although the ability to make polyP is important for survival during quiescence and resistance to diverse environmental stresses, granule genesis is poorly understood. Using quantitative microscopy at high spatial and temporal resolution, we show that granule genesis in Pseudomonas aeruginosa is tightly organized under nitrogen starvation. Following nucleation as many microgranules throughout the nucleoid, polyP granules consolidate and become transiently spatially organized during cell cycle exit. Between 1 and 3 h after nitrogen starvation, a minority of cells have divided, yet the total granule number per cell decreases, total granule volume per cell dramatically increases, and individual granules grow to occupy diameters as large as ∼200 nm. At their peak, mature granules constitute ∼2% of the total cell volume and are evenly spaced along the long cell axis. Following cell cycle exit, granules initially retain a tight spatial organization, yet their size distribution and spacing relax deeper into starvation. Mutant cells lacking polyP elongate during starvation and contain more than one origin. PolyP promotes cell cycle exit by functioning at a step after DNA replication initiation. Together with the universal starvation alarmone (p)ppGpp, polyP has an additive effect on nucleoid dynamics and organization during starvation. Notably, cell cycle exit is temporally coupled to a net increase in polyP granule biomass, suggesting that net synthesis, rather than consumption of the polymer, is important for the mechanism by which polyP promotes completion of cell cycle exit during starvation

A novel technique for reducing false positive detections in CAD-CTC

Computed tomography colonoscopy (CTC) is an emerging alternative to conventional colonoscopy for colorectal cancer screening. A series of computer assisted diagnosis (CAD) techniques have been developed for use in CTC. Although high levels of accuracy for polyp detection have been reported, the problem of excessive false positive detections still warrants attention. We present a CAD-CTC technique that has been developed specifically to reduce the number of false positive detections without compromising polyp detection accuracy. The technique incorporates a novel intermediate stage that restructures initial polyp candidates so that they conform more closely to the shape of actual polyps. The restructuring process causes false positives to expand to include more false positive characteristics, whereas, actual polyps retain their original polyp-like characteristics. An evaluation of the documented technique demonstrated that it can be successfully applied to the majority of polyp candidates, and that its use can reduce the number of false positive detections by up to 57.8%

The use of 3D surface fitting for robust polyp detection and classification in CT colonography

In this paper we describe the development of a computationally efficient computer-aided detection (CAD) algorithm based on the evaluation of the surface morphology that is employed for the detection of colonic polyps in computed tomography (CT) colonography. Initial polyp candidate voxels were detected using the surface normal intersection values. These candidate voxels were clustered using the normal direction, convexity test, region growing and Gaussian distribution. The local colonic surface was classified as polyp or fold using a feature normalized nearest neighborhood classifier. The main merit of this paper is the methodology applied to select the robust features derived from the colon surface that have a high discriminative power for polyp/fold classification. The devised polyp detection scheme entails a low computational overhead (typically takes 2.20 min per dataset) and shows 100% sensitivity for phantom polyps greater than 5 mm. It also shows 100% sensitivity for real polyps larger than 10 mm and 91.67% sensitivity for polyps between 5 to 10 mm with an average of 4.5 false positives per dataset. The experimental data indicates that the proposed CAD polyp detection scheme outperforms other techniques that identify the polyps using features that sample the colon surface curvature especially when applied to low-dose datasets

Colocation and role of polyphosphates and alkaline phosphatase in apatite biomineralization of elasmobranch tesserae

AbstractElasmobranchs (e.g. sharks and rays), like all fishes, grow continuously throughout life. Unlike other vertebrates, their skeletons are primarily cartilaginous, comprising a hyaline cartilage-like core, stiffened by a thin outer array of mineralized, abutting and interconnected tiles called tesserae. Tesserae bear active mineralization fronts at all margins and the tesseral layer is thin enough to section without decalcifying, making this a tractable but largely unexamined system for investigating controlled apatite mineralization, while also offering a potential analog for endochondral ossification. The chemical mechanism for tesserae mineralization has not been described, but has been previously attributed to spherical precursors, and alkaline phosphatase (ALP) activity. Here, we use a variety of techniques to elucidate the involvement of phosphorus-containing precursors in the formation of tesserae at their mineralization fronts. Using Raman spectroscopy, fluorescence microscopy and histological methods, we demonstrate that ALP activity is located with inorganic phosphate polymers (polyP) at the tessera–uncalcified cartilage interface, suggesting a potential mechanism for regulated mineralization: inorganic phosphate (Pi) can be cleaved from polyP by ALP, thus making Pi locally available for apatite biomineralization. The application of exogenous ALP to tissue cross-sections resulted in the disappearance of polyP and the appearance of Pi in uncalcified cartilage adjacent to mineralization fronts. We propose that elasmobranch skeletal cells control apatite biomineralization by biochemically controlling polyP and ALP production, placement and activity. Previous identification of polyP and ALP shown previously in mammalian calcifying cartilage supports the hypothesis that this mechanism may be a general regulating feature in the mineralization of vertebrate skeletons

An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN

Polyp has long been considered as one of the major etiologies to colorectal cancer which is a fatal disease around the world, thus early detection and recognition of polyps plays a crucial role in clinical routines. Accurate diagnoses of polyps through endoscopes operated by physicians becomes a challenging task not only due to the varying expertise of physicians, but also the inherent nature of endoscopic inspections. To facilitate this process, computer-aid techniques that emphasize fully-conventional image processing and novel machine learning enhanced approaches have been dedicatedly designed for polyp detection in endoscopic videos or images. Among all proposed algorithms, deep learning based methods take the lead in terms of multiple metrics in evolutions for algorithmic performance. In this work, a highly effective model, namely the faster region-based convolutional neural network (Faster R-CNN) is implemented for polyp detection. In comparison with the reported results of the state-of-the-art approaches on polyps detection, extensive experiments demonstrate that the Faster R-CNN achieves very competing results, and it is an efficient approach for clinical practice.Comment: 6 pages, 10 figures,2018 International Conference on Pattern Recognitio