Abstract Introduction. Electroporation-based treatments rely on increasing the permeability of the cell membrane by high voltage electric pulses delivered to tissue via electrodes. To ensure that the whole tumor is covered by the sufficiently high electric field, accurate numerical models are built based on individual patient geometry. For the purpose of reconstruction of hepatic vessels from MRI images we searched for an optimal segmentation method that would meet the following initial criteria: identify major hepatic vessels, be robust and work with minimal user input. Materials and methods. We tested the approaches based on vessel enhancement filtering, thresholding, and their combination in local thresholding. The methods were evaluated on a phantom and clinical data.

Results show that thresholding based on variance minimization provides less error than the one based on entropy maximization. Best results were achieved by performing local thresholding of the original de-biased image in the regions of interest which were determined through previous vessel-enhancement filtering. Download free acer aspire one d255 windows 7 starter recovery disc iso software.

In evaluation on clinical cases the proposed method scored in average sensitivity of 93.68%, average symmetric surface distance of 0.89 mm and Hausdorff distance of 4.04 mm. The proposed method to segment hepatic vessels from MRI images based on local thresholding meets all the initial criteria set at the beginning of the study and necessary to be used in treatment planning of electroporation- based treatments: it identifies the major vessels, provides results with consistent accuracy and works completely automatically.

Whether the achieved accuracy is acceptable or not for treatment planning models remains to be verified through numerical modeling of effects of the segmentation error on the distribution of the electric field.

Ultrasensitive responses are common in cellular information transfer because they allow cells to decode extracellular stimuli in an all‐or‐none manner. Biochemical responses are usually analyzed by fitting the Hill equation, and the estimated Hill coefficient is taken as a measure of sensitivity. However, this approach is not appropriate if the response under consideration significantly deviates from the best‐fit Hill equation. In addition, Hill coefficients greater than unity do not necessarily imply ultrasensitive behaviour if basal activation is significant. In order to circumvent these problems we propose a general method for the quantitative analysis of sensitivity, the relative amplification plot, which is based on the response coefficient defined in metabolic control analysis. To quantify sensitivity globally (i.e. Over the whole stimulus range) we introduce the integral‐based relative amplification coefficient.

Introduction large extent by IW (please see Section 3d for a detailed discussion of IW in the Indian Ocean). 15° B rozi I 10°5.' Of the ocean- Numerical experiments using a wind-driven quasi-geostrophic model, J.

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Our relative amplification approach can easily be extended to monotonically decreasing, bell‐shaped or nonsaturated responses. In cellular signal transduction, a stimulus (e.g. An extracellular hormone) brings about intracellular responses such as transcription. These responses may depend on the extracellular hormone concentration in a gradual or an ultrasensitive (i.e. All‐or‐none) manner. In gradual systems, a large relative increase in the stimulus is required to accomplish large relative changes in the response, while a small relative alteration in the stimulus is sufficient in ultrasensitive systems. Ultrasensitive responses are common in cellular information transfer [ -] as this allows cells to reject background noise, while amplifying strong inputs [, ].