Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

Abstract

Purpose:To assess whether glioma volumes from knowledge-based fuzzy c-means (FCM) clustering of multiple MR image classes can provide similar diagnostic efficacy values as manually defined tumor volumes when characterizing gliomas from dynamic susceptibility contrast (DSC) imaging. Materials and Methods:Fifty patients with newly diagnosed gliomas were imaged using DSC MR imaging at 1.5 Tesla. To compare our results with manual tumor definitions, glioma volumes were also defined independently by four neuroradiologists. Using a histogram analysis method, diagnostic efficacy values for glioma grade and expected patient survival were assessed. Results:The areas under the receiver operator characteristics curves were similar when using manual and automated tumor volumes to grade gliomas (P ϭ 0.576 -0.970). When identifying a high-risk patient group (expected survival Ͻ2 years) and a low-risk patient group (expected survival Ͼ2 years), a higher log-rank value from Kaplan-Meier survival analysis was observed when using automatic tumor volumes (14.403; P Ͻ 0.001) compared with the manual volumes (10.650 -12.761; P ϭ 0.001-0.002). Conclusion:Our results suggest that knowledge-based FCM clustering of multiple MR image classes provides a completely automatic, user-independent approach to selecting the target region for presurgical glioma characterization

Evaluation of Apparent Diffusion Coefficient Thresholds for Diagnosis of Medulloblastoma Using Diffusion-Weighted Imaging

“…Extensive literature describes the implementation of CAD for detection and diagnosis of a number of conditions including pulmonary nodules, breast cancer, and aneurysms 2,3 . However, applications of CAD to neuroradiology, and in particular, to brain tumor diagnosis, have been relatively limited to date [4][5][6] . The goal of the present study was to test an analytic technique based on diffusionweighted imaging for classification of pediatric posterior fossa tumors that might be amenable to eventual automated use via CAD.…”

Measurement of Apparent Diffusion Coefficient Values from Diffusion-Weighted MRI: A Comparison of Manual and Semiautomatic Segmentation Methods

“…Similar to the results by Jung et al (19), our results showed higher interobserver reproducibility in the semiautomatic segmentation method than the manual segmentation method on CE-T1WI-based evaluation, even though the gap between them was negligible. Moreover, the automatic segmentation method reportedly has better reproducibility, is less time-consuming, and offers greater benefits in stratifying tumor characteristics (11,20). However, we could not support such an idea when using the semiautomatic segmentation method.…”

“…Our study proved that the T2WI-based semiautomatic segmentation method was the least reliable method, particularly in groups with involved lobes ≤ 2, with partially poorly demarcated tumor borders, with poorly demarcated inner margins of the necrotic portion, and with perilesional edema, while both manual and semiautomatic segmentation methods on CE-T1WI-based evaluation are clinically acceptable for assessment of aforementioned subgroups. Relatively similar T1 and T2 relaxation parameters among pathologic brain lesions, brain edema, and fibrotic tissue are obtained on T2WI, as compared with CE-T1WI (20). It is also known that the differences in signal intensities among the clusters are not sufficient to separate tumor components, such as solid, cystic, or hemorrhagic components, perilesional edema, and perilesional tumor infiltration, automatically on T2WI (10,22).…”

Brain Tumor Segmentation Using Geodesic Region-based Level Set without Re-initialization

“…In order to get more satisfactory results, some algorithms combined features of the medical images, such as symmetry [6,7,10], texture [7,11], spatial accuracy vector [12], mutual information [13], knowledge-based information [14], and so on.…”