ACCURACY AND REPRODUCIBILITY OF BRAIN AND TISSUE VOLUMES USING A MAGNETIC-RESONANCE SEGMENTATION METHOD

Magnetic resonance (MR) imaging now allows the qualitative and quantit ative assessment of the human brain in vivo. As MR imaging resolution has improved, precise measurement of small brain structures has become possible. Methods of measuring brain regions from MR images include b oth manual and semiautomated methods. Despite the development of numer ous volumetric methods, there have been only limited attempts so far t o evaluate the accuracy and reproducibility of these methods. In this study we used phantoms to assess the accuracy of the segmentation proc ess. Our results with simple and complex phantoms indicate an error of 3-5% using either manual or semiautomated techniques. We subsequently used manual and semiautomated volumetric methodologies to study human brain structures in vivo in five normal subjects. Supervised segmenta tion is a semiautomated method that accomplishes the division of MR im ages into several tissue types based on differences in signal intensit y. This technique requires the operator to manually identify points on the MR images that characterize each tissue type, a process known as seeding. However, the use of supervised segmentation to assess the vol umes of gray and white matter is subject to pitfalls. Inhomogeneities of the radiofrequency or magnetic fields can result in misclassificati on of tissue points during the tissue seeding process, limiting the ac curacy and reliability of the segmentation process. We used a structur ed seeding protocol that allowed for field inhomogeneity that produced reduced variation in measured tissue volumes. We used repeated segmen tations to assess intra- and inter-rater reliability, and were able to measure small and large regions of interest with a small degree of va riation. In addition, we demonstrated that measurements are reproducib le with repeat MR acquisitions, with minimal interscan variability. Se gmentation methods can accurately and reliably measure subtle morphome tric changes, and will prove a boon to the study of neuropsychiatric d isorders.