Segmentation results for four slices from the same patient.

<p>The first row shows the original images; the second row is segmentation results by applying Dirichlet process (DP) model to each image individually; the third row is the results of the proposed hierarchical DP (HDP) model, the last row is the segmentation results of random walk.</p

Segmentation results from slices from four patients.

<p>The first row shows the original images, the second row is segmentation results by applying DP model to each image individually, the third row is the results of the proposed model, the last row is the segmentation results of random walk.</p

Object borders from manual tracing and segmentation algorithms for the forth slice.

<p>(a) manual analysis from a physician; (b) automatic results given by DP model; (c) automatic result of HDP model; (d) automatic result of random walk.</p

Segmentation results for four slices from four patient.

<p>The first row shows the original images; the second row is segmentation results by applying Dirichlet process (DP) model to each image individually; the third row is the results of the proposed hierarchical DP (HDP) model, the last row is the segmentation results of random walk.</p

Inference time for presented three methods for segmenting four slices in Section. segmentation from the same patient.

<p>The computation time for DP and random walk is the combined time for segmenting four slices.</p

Segmentation results from slices from four patients.

<p>The first row shows the original images, the second row is segmentation results by applying DP model to each image individually, the third row is the results of the proposed model, the last row is the segmentation results of random walk.</p

Jaccard index for segmented vertebra and lungs.

<p>DP stands for single segmentation model based on Dirichlet process, RW represents random walk model.</p

Controllable Fabrication of Coordination Polymer Particles (CPPs): A Bridge between Versatile Organic Building Blocks and Porous Copper-Based Inorganic Materials

Hierarchically micro-/nanostructured coordination polymer [Cu­(2,5-PDC)­H₂O]_<i>n</i> architectures with tunable morphologies have been successfully prepared by rationally adjusting the preparation parameters, such as the reactant concentration, solvent, surfactant, and reaction temperature. Using simple calcinations of chosen shaped [Cu­(2,5-PDC)­H₂O]_<i>n</i> architectures, we can obtain several porous copper-based inorganic motifs, which show potential applications for the antibacterial field and lithium ion batteries. Therein, CuO-1 can kill the Gram-positive bacteria <i>Bacillus subtilis and Staphylococcus aureus</i> better than other materials. The value for initial discharge capacity of CuO-3 (1160 mAh g^–1) is higher than the theoretical capacity (674 mAh g^–1) and most copper oxide materials. Besides, Cu/C composites also show intense application in the antibacterial and Li-ions uptake-release field, which will provide a widely used method to prepare the nanosystem of carbon-coating or carbon-compositing materials by simple calcinations of shaped precursor coordination polymer particles used under the proper temperature

Object borders by manual tracing and estimates from segmentation algorithms for the third slice.

<p>(a) manual analysis from a physician; (b) automatic results given by DP model; (c) automatic result of HDP model; (d) automatic result of random walk.</p

Jaccard index for segmented objects versus manual tracing.

<p>DP stands for segmentation model based on Dirichlet process, RW represents random walk model.</p