Automatic segmentation and therapy follow-up of cerebral glioma in diffusion-tensor images

G. De Nunzio, M. Donativi, G. Pastore, L. Bello, R. Soffietti, A. Falini, A. Castellano
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引用次数: 3

Abstract

Gliomas are the most common primary brain tumors, with a typical infiltrative growth pattern along white matter (WM) fibers. Diffusion Tensor Imaging (DTI) is sensitive to the directional diffusion of water along WM tracts, which allows the identification of subtle peritumoral glioma infiltration that are not apparent on conventional Magnetic Resonance imaging. The aim of this study was to characterize pathological and healthy tissue in DTI datasets by statistical texture analysis, developing a Computer Assisted Detection (CAD) technique for cerebral glioma. This system, coupled to voxel-based tumor evolution analysis, could allow objective tumor identification and qualitative and quantitative measurements in the follow-up of patients during chemotherapy. In this paper, preliminary results of tumor segmentation and evolution analysis are shown.
脑胶质瘤弥散张量图像的自动分割及治疗随访
胶质瘤是最常见的原发性脑肿瘤,具有典型的沿白质(WM)纤维浸润生长模式。弥散张量成像(Diffusion Tensor Imaging, DTI)对水沿WM束的定向扩散很敏感,可以识别常规磁共振成像不明显的肿瘤周围细微胶质瘤浸润。本研究的目的是通过统计纹理分析来表征DTI数据集中的病理和健康组织,开发一种脑胶质瘤的计算机辅助检测(CAD)技术。该系统与基于体素的肿瘤演变分析相结合,可以在化疗期间对患者进行客观的肿瘤识别和定性定量的随访。本文给出了肿瘤分割和进化分析的初步结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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