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Here, a new computational framework for segmentation of 3D EM datasets is introduced.
Extension of the model could also be imagined to tackle the problem of the joint segmentation of datasets where transcript boundaries and expression level differ.
using the pipeline described in Segmentation of the Dataset.
Successful segmentation of the dataset was conducted using a combination of level-set segmentation filters, combined in a sequential processing pipeline to refine the segmentation at each stage.
Identification of different surfaces within the final ventricular finite-element mesh was achieved by performing a secondary segmentation of the MR data using the same segmentation pipeline described in Segmentation of the Dataset.
For the details of preprocessing and segmentation of copy number dataset, please refer to [ 63].
Concerning the second problem, the spatial segmentation of an IMS dataset, several approaches have been proposed.
Segmentation and extraction of quantitative data from OCT images are challenging [ 23– 25] but are required to facilitate and improve diagnosis.
The vascular segmentation of fetoplacental micro-CT datasets has been previously described in detail (52).
The segmentation of ventricles provides quantitative measures on the changes of ventricles in the brain that form vital diagnosis information.
In Table 4, we provide a summary of quantitative evaluation on several datasets.
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