Segmentation is one of the most studied problems in the field of biomedical image analysis. The new multi-modality semi-automatic segmentation approach is evaluated in the context of high-grade glioblastoma segmentation. The approach combines Random Forest classifiers, trained by the user by placing several brushstrokes in the image, with the active contour segmentation algorithm. This paper describes new extensions to the ITK-SNAP interactive image visualization and segmentation tool that support semi-automatic segmentation of multi-modality imaging datasets in a way that utilizes information from all available modalities simultaneously. However, few existing 3D image analysis tools support semi-automatic segmentation of multi-modality imaging data.
Teampaper snap multiple annotations manual#
In applications where fully automatic segmentation algorithms are unavailable or fail to perform at desired levels of accuracy, semi-automatic segmentation can be a time-saving alternative to manual segmentation, allowing the human expert to guide segmentation, while minimizing the effort expended by the expert on repetitive tasks that can be automated. Multi-modality imaging datasets, in which multiple imaging measures are available at each spatial location, are increasingly common, particularly in MRI. Obtaining quantitative measures from biomedical images often requires segmentation, i.e., finding and outlining the structures of interest.
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