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

Abstract

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.

Figures

Fig. 1

ITK-SNAP tool (version 3.4) with four MRI modalities from a BRATS tumor dataset loaded.

Fig. 2

ITK-SNAP tool during speed image computation in the semiautomatic segmentation mode. Examples of three labels are seen on the selected MRI slices, and the speed image is visualized using the blue (negative) to white (positive) colormap. The speed image is computed for the ET and necrosis as foreground labels, and other labels mapped to background.

Fig. 3

ITK-SNAP tool after completion of the segmentation of the edema (yellow), ET (blue) and necrosis (green).

Fig. 4

Quantitative evaluation of tumor segmentation accuracy by two raters using ITK-SNAP. Intra-rater reliability (repeat segmentation of same images by Rater 1), inter-rater reliability (segmentation of same images by Rater 1 and 2), and agreement between Rater 1 and the BRATS reference segmentation are reported for three labels used in the BRATS framework.