A fuzzy feature fusion method for auto-segmentation of gliomas with multi-modality diffusion and perfusion magnetic resonance images in radiotherapy

Abstract

The difusion and perfusion magnetic resonance (MR) images can provide functional information about tumour and enable more sensitive detection of the tumour extent. We aimed to develop a fuzzy feature fusion method for auto-segmentation of gliomas in radiotherapy planning using multi-parametric functional MR images including apparent difusion coefcient (ADC), fractional anisotropy (FA) and relative cerebral blood volume (rCBV). For each functional modality, one histogram-based fuzzy model was created to transform image volume into a fuzzy feature space. Based on the fuzzy fusion result of the three fuzzy feature spaces, regions with high possibility belonging to tumour were generated automatically. The auto-segmentations of tumour in structural MR images were added in fnal autosegmented gross tumour volume (GTV). For evaluation, one radiation oncologist delineated GTVs for nine patients with all modalities. Comparisons between manually delineated and auto-segmented GTVs showed that, the mean volume diference was 8.69% (±5.62%); the mean Dice’s similarity coefcient (DSC) was 0.88 (±0.02); the mean sensitivity and specifcity of auto-segmentation was 0.87 (±0.04) and 0.98 (±0.01) respectively. High accuracy and efciency can be achieved with the new method, which shows potential of utilizing functional multi-parametric MR images for target defnition in precision radiation treatment planning for patients with gliomas.