title: Cerebral tumor volume calculations using planimetric and eigenimage analysis.
abstract: Volume determination in cerebral tumors requires accurate and reproducible segmentation. This task has been traditionally accomplished using planimetric methods which define the boundary of the lesion using thresholding and edge detection schemes. These methods lack accuracy and reproducibility when the contrast between the lesion and surrounding tissue is not maximized. Because of this limitation contrast agents are used providing reproducible results for the enhancing portion of the lesion. A novel approach for volume determination has been developed (eigenimage filter) which segments a desired feature (tissue type) from surrounding undesired features in a sequence of images. This method corrects for partial volume effects and has been shown to provide accurate and reproducible volume determinations. In addition, the eigenimage filter does not require the use of contrast and has the capability to segment a lesion into multiple regions. This allows different components of the lesion to be included and monitored in treatment. In this study planimetric methods and the eigenimage filter were compared for segmenting cerebral tumors and determining their volumes. The planimetric methods were reproducible in determining volumes for the enhancing portion of the lesion with interobserver percent differences < 8% and intraobserver percent differences < 4%. The eigenimage filter had interobserver percent differences < 7% and intraobserver percent differences < 3%. In the eigenimage procedure both the enhancing portion of the lesion as well as additional regions within the lesion were identified. Comparing the results obtained from the two methods demonstrated good agreement for presurgical studies (percent differences < 9%). When comparing postsurgical studies large differences were seen. In the postsurgical studies the eigenimage method allowed multiple regions to be followed in subsequent MRI and in two patients showed a volume change that suggested tumor recurrence more clearly. Since the amount of information obtained using the eigenimage filter may allow a more complete assessment of the lesion, it is suggested that it could improve the clinical evaluation of cerebral tumors.
authors.affiliation: Henry Ford Health System, Midwest Neuro-Oncology Center, Detroit, Michigan 48202, USA.
authors.forename: D J
authors.initials: DJ
authors.lastname: Peck
authors.forename: J P
authors.initials: JP
authors.lastname: Windham
authors.forename: L L
authors.initials: LL
authors.lastname: Emery
authors.forename: H
authors.initials: H
authors.lastname: Soltanian-Zadeh
authors.forename: D O
authors.initials: DO
authors.lastname: Hearshen
authors.forename: T
authors.initials: T
authors.lastname: Mikkelsen
mesh_terms: D055592:Biophysical Phenomena; D001703:Biophysics; D001932:Brain Neoplasms / Q000473:pathology*; D005069:Evaluation Studies as Topic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000706:statistics & numerical data; D009364:Neoplasm Recurrence, Local / Q000175:diagnosis / Q000473:pathology; D015203:Reproducibility of Results