Flow chart of the image segmentation scheme.

<p>The proposed segmentation algorithm includes five consecutive steps: DCE micro-CT images acquisition, data dimension reduction, supervoxel generation, supervoxel classification, and target organs’ extraction.</p

Quantitative evaluation of the proposed methods by comparison to manual segmentation.

<p>‘SM1’ and ‘RM1’ represents the comparison of the automatic segmentation by SVM and RF with the manual segmentation (M1), respectively. ‘M1M3’ compares the manual segmentations of two independent experts. ‘M1M2’ compares two manual segmentation repetitions of one expert. (A) Dice similarity coefficient. (B) False positive ratio. (C) False negative ratio. (* Indicates <i>p</i> < 0.05.)</p

Visual comparison of the segmentation results with the reference datasets, shown in 2-D images.

<p>The organ boundaries of manual segmentation (M1) and automatic segmentation based on the SVM and the RF are superimposed on two coronal images (A, B) and two sagittal images (C, D).</p

Dynamic contrast enhancement procedure after contrast agent administration.

<p>(A) Representative coronal micro-CT images before contrast agent injection and at 0 s, 50 s, 100 s, 150 s, and 200 s post-contrast injection. The image at 0 s was acquired during the inflow of contrast agent. All of the images are displayed with the same gray scale window. (B) The relative signal enhancement versus time curves of regions depicted by the arrows in (A) with the same colors.</p

Number of supervoxels for each category chosen to constitute the total data set.

<p>Number of supervoxels for each category chosen to constitute the total data set.</p

Visual comparison of the segmentation results with the reference datasets, shown in 3-D isosurface rendering.

<p>Left column: left lateral view. Right column: posterior view. Top row: manual segmentation (M1). Middle row: segmentation obtained by the SVM. Bottom row: segmentation obtained by the RF.</p

Influence of the number of training samples on segmentation accuracy.

<p>10%, 30%, 50%, 70%, and 90% of the total samples of each category were selected randomly from the total data set and consisted of training sets for classification. Each case was repeated five times. The means and standard deviations of Dice similarity coefficients for the heart, liver, spleen, lung, and kidney were calculated (compared with M1). (A) The DSC of the organs classified by SVM. (B) The DSC of the organs classified by RF. For the lung, one value at 10% and two values at 30% were excluded from statistics because it failed to extract the lung by post-processing.</p

Refractory Period Modulates the Spatiotemporal Evolution of Cortical Spreading Depression: A Computational Study

<div><p>Cortical spreading depression (CSD) is a pathophysiological phenomenon, which underlies some neurological disorders, such as migraine and stroke, but its mechanisms are still not completely understood. One of the striking facts is that the spatiotemporal evolution of CSD wave is varying. Observations in experiments reveal that a CSD wave may propagate through the entire cortex, or just bypass some areas of the cortex. In this paper, we have applied a 2D reaction-diffusion equation with recovery term to study the spatiotemporal evolution of CSD. By modulating the recovery rate from CSD in the modeled cortex, CSD waves with different spatiotemporal evolutions, either bypassing some areas or propagating slowly in these areas, were present. Moreover, spiral CSD waves could also be induced in case of the transiently altered recovery rate, i.e. block release from the absolute refractory period. These results suggest that the refractory period contributes to the different propagation patterns of CSD, which may help to interpret the mechanisms of CSD propagation.</p></div

The influence of different Ca²⁺ flows on CSD-triggered Ca²⁺ waves (CSDCWs).

<p>A typical CSDCW is characterized as the significant elevation of Ca²⁺ in the ICS at successive astrocytes in the network (A), associated with the increase of Ca²⁺ in the ER (B), the increase of IP₃ in the ICS (D) and the decrease of Ca²⁺ in the ECS (C). CICR inhibition shortens the duration of increased Ca²⁺ in the ICS (E), slows the recovery of Ca²⁺ in the ER (F), decreases Ca²⁺ in the ECS more than in the control condition (G) and increases the duration of increased IP₃ in the ICS (H). SERCA inhibition increases the amplitude of Ca²⁺ in the ICS (I) and decreases that in the ER (J). Ca²⁺ in the ECS is increased compared to the control (K). Because high Ca²⁺ in the ICS would inhibit the process of CICR, the increase of IP₃ in the ICS is shortened (L). After VGCCs inhibition, Ca²⁺ is largely weakened in the ICS (M), in the ER (N) and in the ECS (O). The changes of IP₃ are also shortened because of the low Ca²⁺ in the ICS (P).</p

The influence of different Ca²⁺ flows on CASs.

<p>By inhibiting CICR (dashed triangle), the frequency (A) and amplitude (B) of CASs decrease, but the duration (C) increases. CASs do not occur when CICR is inhibited more than 95%. By inhibiting SERCA (dashed circle), the frequency (A) of CASs increases, but both the amplitude (B) and the duration (C) decrease. CASs do not occur when SERCA is inhibited more than 70%. Inhibiting VGCCs (solid star) has little effect on the amplitude (B) and duration (C) but great on frequency (A) before CASs disappear. CASs do not occur when VGCCs are inhibited more than 45%.</p