COMPARISON OF SUPERVISED MRI SEGMENTATION METHODS FOR TUMOR VOLUME DETERMINATION DURING THERAPY

Two different multispectral pattern recognition methods are used to segment magnetic resonance images (MRI) of the brain for quantitative estimation of tumor volume and volume changes with therapy. A supervised k-nearest neighbor (kNN) rule and a semi-supervised fuzzy c-means (SFCM) method are used to segment MRI slice data, Tumor volumes as determined by the kNN and SFCM segmentation methods are compared with two reference methods, based on image grey scale, as a basis for an estimation of ground truth, namely: (a) a commonly used seed growing method that is applied to the contrast enhanced T-1-weighted image, and (b) a manual segmentation method using a custom-designed graphical user interface applied to the same raw image (T-1-weighted) dataset, Emphasis is placed on measurement of intra and inter observer reproducibility using the proposed methods. Intra-and interobserver variation for the kNN method was 9% and 5%, respectively, The results for the SFCM method was a little better at 6% and 4%, respectively, For the seed growing method, the intra-observer variation was 6% and the interobserver variation was 17%, significantly larger when compared with the multispectral methods. The absolute tumor volume determined by the multispectral segmentation methods was consistently smaller than that observed for the reference methods. The results of this study are found to be very patient case-dependent. The results for SFCM suggest that it should be useful for relative measurements of tumor volume during therapy, but further studies are required, This work demonstrates the need for minimally supervised or unsupervised methods for tumor volume measurements.