MONITORING BRAIN-TUMOR RESPONSE TO THERAPY USING MRI SEGMENTATION

The performance evaluation of a semi-supervised fuzzy c-means (SFCM) c lustering method for monitoring brain tumor volume changes during the course of routine clinical radiation-therapeutic and chemo-therapeutic regimens is presented, The tumor volume determined using the SFCM met hod was compared with the volume estimates obtained using three other methods: (a) a k nearest neighbor (kNN) classifier, b) a grey level th resholding and seed growing (ISG-SG) method and c) a manual pixel labe ling (GT) method for ground truth estimation, The SFCM and kNN methods are applied to the multispectral, contrast enhanced T-1, proton densi ty, and T-2 weighted, magnetic resonance images (MRI) whereas the ISG- SG and GT methods are applied only to the contrast enhanced T-1 weight ed image, Estimations of tumor volume were made on eight patient cases with follow-up MRI scans performed over a 32 week interval during tre atment, The tumor cases studied include one meningioma, two brain meta stases and five gliomas, Comparisons with manually labeled ground trut h estimations showed that there is a limited agreement between the seg mentation methods for absolute tumor volume measurements when using im ages of patients after treatment, The average intraobserver reproducib ility for the SFCM, kNN and ISG-SG methods was found to be 5.8%, 6.6% and 8.9%, respectively, The average of the interobserver reproducibili ty of these methods was found to be 5.5%, 6.5% and 11.4%, respectively , For the measurement of relative change of tumor volume as required f or the response assessment, the multi-spectral methods kNN and SFCM ar e therefore preferred over the seed growing method. (C) 1997 Elsevier Science Inc.