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.

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training

“…The final dataset included 3; 220 mpMRI scans from 805 subjects, with the 4 structural modalities previously mentioned for each subject ( Table 1 ). The ground truth brain masks of all the included scans were obtained through semi-automatic annotation using the ITK-SNAP ( itksnap.org ) software ( Yushkevich et al, 2006 ; Yushkevich et al, 2019 ) and approved by a board-certified neuro-radiologist (M.B.) with more than 10 years of experience working with gliomas.…”

Neurofilament light chain is a promising serum biomarker in spinocerebellar ataxia type 3

“…We obtained the volumes of gray matter, white matter and CSF filling areas from each subject, which were used to calculate the total intracranial volumes (TIV). The volumes of brainstem and cerebellum were manually measured using ITK-SNAP software (Version 3.6.0) [25]. In the correlation analyses between serum NfL concentrations and the volumes of brainstem and cerebellum, the TIV was regressed out as a covariate to eliminate the influence of TIV.…”

Accuracy and practical aspects of semi- and fully automatic segmentation methods for resected brain areas

“…Semi-automatic segmentation was performed with ITK-SNAP toolbox version 3.6 (further referred to as SNAP) [ 16 ]. The “region competition” segmentation approach as well as the active contour evolution algorithm and their implementation in the ITK-SNAP has been previously described in detail [ 16 , 38 ]. Briefly, the rater defines a segmentation domain to restrict the algorithm to a volumetric ROI.…”