Quantitation of T2 lesion load in patients with multiple sclerosis: A novel semiautomated segmentation technique

Rationale and Objectives. The authors designed a segmentation technique tha t requires only minimal operator input at the initial and final supervision stages of segmentation and has computer-driven segmentation as the primary determinant of lesion boundaries. The technique was applied to compute tot al T2-hyperintense lesion volumes in patients with multiple sclerosis (MS), A semi-automated segmentation technique is presented and shown to have a t est-retest reliability of <5%. Materials and Methods. The method used a single segmented section with MS l esions. A probabilistic neural net performed segmentation into four tissue classes after supervised training. This reference section was deconstructed into the entire set of possible 4 x 4-pixel subregions, which was used to segment all-brain sections in steps of 4 x 4-pixel, adjacent image blocks, Intra- and interimage variabilities were tested by using 3-mm-thick, T2-wei ghted, dual-echo, spin-echo MR images from five patients, each of whom was imaged twice on the same day. Five different reference sections and three t emporally separated training sessions involving the same reference section were used to test the segmentation technique. Results. The coefficient of variation ranged from 0.013 to 0.068 (mean +/- standard deviation, 0.037 +/- 0.039) for results from five different refere nce sections for each brain and from 0.007 to 0.037 (mean, 0.027 +/- 0.021) for brains segmented with the same reference section on three temporally s eparated occasions. Test-retest (intra-imaging) reliability did not exceed 5% (except for a small lesion load of 1 cm(3) in one patient). Interimaging differences were approximately 10%. Conclusion. The segmentation technique yielded intra-imaging variabilities (2%-3%, except for very small MS lesion loads) that compare favorably with previously published results. New repositioning techniques that minimize im aging-repeat imaging variability could make this approach attractive for re solving MS lesion detection problems.