Sparse Patch-Based Label Propagation for Accurate Prostate Localization in CT Images

In this paper, we propose a new prostate computed tomography (CT) segmentation method for image guided radiation therapy. The main contributions of our method lie in the following aspects. 1) Instead of using voxel intensity information alone, patch-based representation in the discriminative feature space with logistic sparse LASSO is used as anatomical signature to deal with low contrast problem in prostate CT images. 2) Based on the proposed patch-based signature, a new multi-atlases label fusion method formulated under sparse representation framework is designed to segment prostate in the new treatment images, with guidance from the previous segmented images of the same patient. This method estimates the prostate likelihood of each voxel in the new treatment image from its nearby candidate voxels in the previous segmented images, based on the nonlocal mean principle and sparsity constraint. 3) A hierarchical labeling strategy is further designed to perform label fusion, where voxels with high confidence are first labeled for providing useful context information in the same image for aiding the labeling of the remaining voxels. 4) An online update mechanism is finally adopted to progressively collect more patient-specific information from newly segmented treatment images of the same patient, for adaptive and more accurate segmentation. The proposed method has been extensively evaluated on a prostate CT image database consisting of 24 patients where each patient has more than 10 treatment images, and further compared with several state-of-the-art prostate CT segmentation algorithms using various evaluation metrics. Experimental results demonstrate that the proposed method consistently achieves higher segmentation accuracy than any other methods under comparison

Junction Point Detection Algorithm for SAR Image

In this paper, we propose a novel junction point detector based on an azimuth consensus for remote sensing images. To eliminate the impact of noise and some noncorrelated edges of SAR image, an azimuth consensus constraint is developed. In addition to detecting the locations of junctions at the subpixel level, this operator recognizes their structures as well. A new formula that includes a minimization criterion for the total weighted distance is proposed to compute the locations of junction points accurately. Compared with other well-known detectors, including Forstner, JUDOCA, and CPDA, the experimental results indicate that our operator outperforms them both in location accuracy of junction points and in angle accuracy of branch edges. Moreover, our method possesses satisfying robustness to the impact of noise and changes of the SAR images. Our operator can be potentially used to solve a number of problems in computer vision, such as SAR image registration, wide-baseline matching, and UAV navigation system

Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model

Computed tomography (CT) imaging is an essential tool in various clinical diagnoses and radiotherapy treatment planning. Since CT image intensities are directly related to positron emission tomography (PET) attenuation coefficients, they are indispensable for attenuation correction (AC) of the PET images. However, due to the relatively high dose of radiation exposure in CT scan, it is advised to limit the acquisition of CT images. In addition, in the new PET and magnetic resonance (MR) imaging scanner, only MR images are available, which are unfortunately not directly applicable to AC. These issues greatly motivate the development of methods for reliable estimate of CT image from its corresponding MR image of the same subject. In this paper, we propose a learning-based method to tackle this challenging problem. Specifically, we first partition a given MR image into a set of patches. Then, for each patch, we use the structured random forest to directly predict a CT patch as a structured output, where a new ensemble model is also used to ensure the robust prediction. Image features are innovatively crafted to achieve multi-level sensitivity, with spatial information integrated through only rigid-body alignment to help avoiding the error-prone inter-subject deformable registration. Moreover, we use an auto-context model to iteratively refine the prediction. Finally, we combine all of the predicted CT patches to obtain the final prediction for the given MR image. We demonstrate the efficacy of our method on two datasets: human brain and prostate images. Experimental results show that our method can accurately predict CT images in various scenarios, even for the images undergoing large shape variation, and also outperforms two state-of-the-art methods

Accurate Segmentation of CT Male Pelvic Organs via Regression-Based Deformable Models and Multi-Task Random Forests

Segmenting male pelvic organs from CT images is a prerequisite for prostate cancer radiotherapy. The efficacy of radiation treatment highly depends on segmentation accuracy. However, accurate segmentation of male pelvic organs is challenging due to low tissue contrast of CT images, as well as large variations of shape and appearance of the pelvic organs. Among existing segmentation methods, deformable models are the most popular, as shape prior can be easily incorporated to regularize the segmentation. Nonetheless, the sensitivity to initialization often limits their performance, especially for segmenting organs with large shape variations. In this paper, we propose a novel approach to guide deformable models, thus making them robust against arbitrary initializations. Specifically, we learn a displacement regressor, which predicts 3D displacement from any image voxel to the target organ boundary based on the local patch appearance. This regressor provides a nonlocal external force for each vertex of deformable model, thus overcoming the initialization problem suffered by the traditional deformable models. To learn a reliable displacement regressor, two strategies are particularly proposed. 1) A multi-task random forest is proposed to learn the displacement regressor jointly with the organ classifier; 2) an auto-context model is used to iteratively enforce structural information during voxel-wise prediction. Extensive experiments on 313 planning CT scans of 313 patients show that our method achieves better results than alternative classification or regression based methods, and also several other existing methods in CT pelvic organ segmentation

Analysis of relationship among visual evoked potential, oscillatory potential and visual acuity under stimulated weightlessness

AIM: To observe the influence of head-down tilt simulated weightlessness on visual evoked potential(VEP), oscillatory potentials(OPs)and visual acuity, and analyse the relationship among them. METHODS: Head-down tilt for -6° was adopted in 14 healthy volunteers. Distant visual acuity, near visual acuity, VEP and OPs were recorded before, two days and five days after trial. The record procedure of OPs followed the ISCEV standard for full-field clinical electroretinography(2008 update). RESULTS: Significant differences were detected in the amplitude of P100 waves and ∑OPs among various time points(P<0.05). But no relationship was observed among VEP, OPs and visual acuity. CONCLUSION: Head-down tilt simulated weightlessness induce the rearrange of blood of the whole body including eyes, which can make the change of visual electrophysiology but not visual acuity

Mesorhizobium septentrionale sp nov and Mesorhizobium temperatum sp nov., isolated from Astragalus adsurgens growing in the northern regions of China

Ninety-five rhizobial strains isolated from Astragalus adsurgens growing in the northern regions of China were classified into three main groups, candidate species 1, 11 and 111, based on a polyphasic approach. Comparative analysis of full-length 16S rRNA gene sequences of representative strains showed that candidate species I and 11 were Mesorhizobium, while candidate species 111, which consisted of non-nodulating strains, was closely related to Agrobacterium tumefaciens. The phylogenetic relationships of the three candidate species and some related strains were also confirmed by the sequencing of glnA genes, which were used as an alternative chromosomal marker. The DNA-DNA relatedness was between 11.3 and 47-1 % among representative strains of candidate species I and 11 and the type strains of defined Mesorhizobium species. Candidate III had DNA relatedness of between 4(.)3 and 25(.)2 % with type strains of Agrobacterium tumefaciens and Agrobacterium rubi. Two novel species are proposed to accommodate candidate species I and 11, Mesorhizobium septentrionale sp. nov. (type strain, SIDW014(T) =CCBAU 11014(T) = HAMBI 2582(T)) and Mesorhizobium temperatum sp. nov. (type strain, SIDW018(T) = CCBAU 11018(T) =HAMBI 2583(T)), respectively. At least two distinct nodA sequences were identified among the strains. The numerically dominant nodA sequence type was most similar to that from the Mesorhizobium tianshanense type strain and was identified in strains belonging to the two novel species as well as other, as yet, undefined genome types. Host range studies indicate that the different nodA sequences correlate with different host ranges. Further comparative studies with the defined Agrobacterium species are needed to clarify the taxonomic identity of candidate species 111

Synergetic treatment of dye contaminated wastewater using microparticles functionalized with carbon nanotubes/titanium dioxide nanocomposites

This journal is © The Royal Society of Chemistry. The highly efficient treatment of azo dye contaminated wastewater from the textile industry is an important but challenging problem. Herein, polydimethylsiloxane (PDMS) microparticles, incorporating multiple-walled carbon nanotubes/titanium dioxide (MWCNTs/TiO2) nanocomposites, were successfully synthesized to treat wastewater containing Rhodamine B (RhB) dyes in a synergetic approach, by combining sorption and photocatalytic degradation. The surfactant wrapping sol-gel method was applied to synthesize MWCNTs/TiO2 nanocomposites with TiO2 nanoparticles evenly distributed on the surface of the MWCNTs. The PDMS microparticles were fabricated with an oil-in-water (O/W) single emulsion template, using needle-based microfluidic devices. MWCNTs/TiO2 nanocomposites (at a weight ratio of 1%, and 2%, respectively) were mixed with the PDMS precursor as the dispersed phase, and an aqueous solution of polyvinyl alcohol (PVA) was used as the continuous phase. Highly monodispersed microparticles, with average diameters of 692.7 μm (Coefficient of Variation, CV = 0.74%) and 678.3 μm (CV = 1.04%), were formed at an applied flow rate of the dispersed and continuous phase of 30 and 200 μL min-1, respectively. The fabricated hybrid microparticles were employed for the treatment of RhB, involving a dark equilibrium for 5 hours and UV irradiation for 3 hours. The experimental conditions of applied PDMS type, mass loading amount, treatment duration, photodegradation kinetics, initial concentration of pollutants and environmental pH values were investigated in this work. The PDMS microparticles with 2 wt% MWCNTs/TiO2 nanocomposites can exhibit a removal efficiency of 85%. Remarkably, an efficiency of 70% can be retained after the microparticles have been recycled and reused for 3 cycles. The PDMS-MWCNTs/TiO2 microparticles possess a superior performance over conventional treatment approaches for dye contaminated wastewater, especially in recyclability and the prevention of secondary pollution. This work provides a feasible and eco-friendly route for developing an efficient and low-cost microfluidic method for treating complicated water environmental systems