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User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP

Abstract

ITK-SNAP is an interactive software tool for manual and semi-automatic segmentation of 3D medical images. This paper summarizes major new features added to ITK-SNAP over the last decade. The main focus of the paper is on new features that support semi-automatic segmentation of multi-modality imaging datasets, such as MRI scans acquired using different contrast mechanisms (e.g., T1, T2, FLAIR). The new functionality uses decision forest classifiers trained interactively by the user to transform multiple input image volumes into a foreground/background probability map; this map is then input as the data term to the active contour evolution algorithm, which yields regularized surface representations of the segmented objects of interest. The new functionality is evaluated in the context of high-grade and low-grade glioma segmentation by three expert neuroradiogists and a non-expert on a reference dataset from the MICCAI 2013 Multi-Modal Brain Tumor Segmentation Challenge (BRATS). The accuracy of semi-automatic segmentation is competitive with the top specialized brain tumor segmentation methods evaluated in the BRATS challenge, with most results obtained in ITK-SNAP being more accurate, relative to the BRATS reference manual segmentation, than the second-best performer in the BRATS challenge; and all results being more accurate than the fourth-best performer. Segmentation time is reduced over manual segmentation by 2.5 and 5 times, depending on the rater. Additional experiments in interactive placenta segmentation in 3D fetal ultrasound illustrate the generalizability of the new functionality to a different problem domain.

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Notes

  1. 1.

    The number of articles citing the original ITK-SNAP paper (Yushkevich et al. 2006) reported by Scopus (scopus.com) as of 3/12/2018 was 1944; the number of citations reported by Google Scholar (scholar.google.com) was 2822.

  2. 2.

    Note that active contour segmentations of individiual structures generated in the semi-automatic mode can be exported in a way that retains partial volume information.

  3. 3.

    https://www.slicer.org/wiki/Documentation/4.8/Modules/SimpleRegionGrowingSegmentation

  4. 4.

    https://www.slicer.org/wiki/Documentation/4.4/Extensions/Wasp

  5. 5.

    https://www.slicer.org/wiki/Documentation/Nightly/Extensions/SegmentationWizard

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Acknowledgements

This work was supported by NIH grants R01 EB014346, R01 EB017255, K01 ES026840, and U01 HD087180. We gratefully acknowledge the many researchers and developers who contributed to ITK-SNAP software over the past 20 years. A full list of contributors is at http://www.itksnap.org/credits. We thank the organizers of the MICCAI BRATS 2012 and 2013 challenges for providing this valuable public dataset, and specifically Prof. Dr. Bjoern Menze at TU München for his advice and assistance with regard to BRATS reference data evaluations.

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Correspondence to Paul A. Yushkevich.

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Yushkevich, P.A., Pashchinskiy, A., Oguz, I. et al. User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP. Neuroinform 17, 83–102 (2019). https://doi.org/10.1007/s12021-018-9385-x

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Keywords

  • Image segmentation
  • Semi-automatic segmentation
  • Gliomas
  • Software
  • MRI