Performance evaluation of automated brain tumor detection systems with expert delineations and interobserver variability analysis in diseased patients on magnetic resonance imaging

Intervention by human expert has turned out to be essential for computerized analysis systems desiring to be approved by medical regulatory bodies. Further, to validate the performance of automated diagnosis systems, interobserver variability analysis is critically important. The purpose of this article is twofold: (i) firstly to perform interobserver variability analysis of two experienced Radiologists interpreting lesion boundary in brain magnetic resonance images; (ii) secondly, to evaluate the performance of recently proposed automated lesion segmentation model with that of the two experienced Radiologists who performed manual delineations of lesion boundary. Experiments were conducted on the database consisting of 80 real-time brain images with glioma tumor acquired using magnetic resonance imaging (MRI). Extensive statistical analysis such as the two tailed T-test, analysis of variance (ANOVA) test, Mann-Whitney U test, regression and correlation tests, etc. are performed to compare the lesions detected manually by experts and that by the automated method. Furthermore, three quantitative measures namely, dice similarity index, Jaccard coefficient, and Hausdorff distance are used to evaluate the automated lesion detection method. The experimental results show that the lesion boundaries detected by the automated method are very close to the manual delineations provided by the expert Radiologists. It is concluded that the automated systems for brain lesion detection can be utilized as a part of routine clinical practice to help the medical professionals in determining the exact location and area of lesions in brain MRI images.