ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images

Obtaining quantitative measures from biomedical images often requires segmentation, i.e., finding and outlining the structures of interest. Multi-modality imaging datasets, in which multiple imaging measures are available at each spatial location, are increasingly common, particularly in MRI. In app...

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Bibliographic Details
Published in:2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2016; pp. 3342 - 3345
Main Authors: Yushkevich, Paul A., Yang Gao, Gerig, Guido
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 01.08.2016
Subjects:
Active contours
Algorithms
Brain Neoplasms - diagnostic imaging
Humans
Image Processing, Computer-Assisted - methods
Image segmentation
Imaging, Three-Dimensional - methods
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Manuals
Multimodal Imaging - methods
Software
Three-dimensional displays
Tumors
ISSN:1557-170X, 2694-0604, 2694-0604
Online Access:Get full text
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Summary:Obtaining quantitative measures from biomedical images often requires segmentation, i.e., finding and outlining the structures of interest. Multi-modality imaging datasets, in which multiple imaging measures are available at each spatial location, are increasingly common, particularly in MRI. 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. However, few existing 3D image analysis tools support semi-automatic segmentation of multi-modality imaging data. 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. The approach combines Random Forest classifiers, trained by the user by placing several brushstrokes in the image, with the active contour segmentation algorithm. The new multi-modality semi-automatic segmentation approach is evaluated in the context of high-grade glioblastoma segmentation.
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ISSN:1557-170X
2694-0604
2694-0604
DOI:10.1109/EMBC.2016.7591443