Volumetric glioma quantification: comparison of manual and semi-automatic tumor segmentation for the quantification of tumor growth. (November 2015)

• Record Type: Journal Article
• Authors: Odland, Audun; Server, Andres; Saxhaug, Cathrine; Breivik, Birger; Groote, Rasmus; Vardal, Jonas; Larsson, Christopher; Bjørnerud, Atle
• Is Part Of: Acta radiologica. Volume 56:Number 11(2015 Nov.)
• Page Start: 1396
• Page End: 1403
• Publication Date: 2015-11
• Subjects: Adults -- magnetic resonance imaging (MRI) -- brain -- central nervous system (CNS) -- 3D computer applications -- radiation therapy; Radiology, Medical -- Periodicals; Radiography, Medical -- Periodicals; Radiotherapy -- Periodicals; 616.0757
• DOI: 10.1177/0284185114554822
• Languages: English
• ISSNs: 0284-1851

Abstract

Background: Volumetric magnetic resonance imaging (MRI) is now widely available and routinely used in the evaluation of high-grade gliomas (HGGs). Ideally, volumetric measurements should be included in this evaluation. However, manual tumor segmentation is time-consuming and suffers from inter-observer variability. Thus, tools for semi-automatic tumor segmentation are needed. Purpose: To present a semi-automatic method (SAM) for segmentation of HGGs and to compare this method with manual segmentation performed by experts. The inter-observer variability among experts manually segmenting HGGs using volumetric MRIs was also examined. Material and Methods: Twenty patients with HGGs were included. All patients underwent surgical resection prior to inclusion. Each patient underwent several MRI examinations during and after adjuvant chemoradiation therapy. Three experts performed manual segmentation. The results of tumor segmentation by the experts and by the SAM were compared using Dice coefficients and kappa statistics. Results: A relatively close agreement was seen among two of the experts and the SAM, while the third expert disagreed considerably with the other experts and the SAM. An important reason for this disagreement was a different interpretation of contrast enhancement as either surgically-induced or glioma-induced. The time required for manual tumor segmentation was an average of 16 min per scan. Editing of the tumor masks produced by the SAM required an average of less than 2 min per sample. Conclusion: Manual segmentation of HGG is very time-consuming and using the SAM could increase the efficiency of this process. However, the accuracy of the SAM ultimately depends on the expert doing the editing. Our study confirmed a considerable inter-observer variability among experts defining tumor volume from volumetric MRIs.