Glioblastoma MRT exploration based on segmentation methods' comparison: Towards an advanced clinical aided tool

Hana Bouchouicha, Olfa Ghribi, A. Hamida, C. Mhiri, M. Dammak, K. B. Mahfoudh, O. Kammoun
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Abstract

Glioblastoma delineation and its related active region specification are still a real challenge and so difficult essentially due to their multiform aspect. in fact, this type of tumors is very invasive and appears as non-enhancing region and with various forms on magnetic resonance imaging modalities. Thus, Glioblastoma segmentation is challenging especially in differentiating between white matter and edema, necrosis and gray matter due to their homogeneity in intensity and texture. An accurate delineation of the tumor is necessary for the tumor progress evaluation and medical treatment efficacy assessment. in addition, a precise limitation of the tumor is mandatory in surgical and Radio Therapies. Manual segmentation methods have been always used and require radiologist intervention and could be also used as reference. our attention was for the benefits to extract from the semi-Automatic segmentation Methods and the Fully Automatic segmentation Methods, and this would yield a real complementarity giving hence one complete and rich convivial clinical aided tool. This paper presents therefore a useful review of these methods proposed for the Glioblastoma MRI segmentation.
基于分割方法比较的胶质母细胞瘤MRT探查:迈向一种先进的临床辅助工具
胶质母细胞瘤的描述及其相关活性区域的描述仍然是一个真正的挑战,而且由于其多形式的方面,因此非常困难。事实上,这种类型的肿瘤具有很强的侵袭性,在磁共振成像模式上表现为非增强区域和各种形式。因此,胶质母细胞瘤的分割具有挑战性,特别是在区分白质与水肿、坏死和灰质时,由于它们在强度和质地上的同质性。准确描述肿瘤是评价肿瘤进展和药物治疗效果的必要条件。此外,在外科和放射治疗中,肿瘤的精确限制是强制性的。人工分割是目前常用的分割方法,但需要放射科医师介入,也可作为参考。我们关注的是从半自动分割方法和全自动分割方法中提取的好处,这将产生真正的互补,从而提供一个完整而丰富的临床辅助工具。因此,本文对胶质母细胞瘤MRI分割提出的这些方法进行了有益的回顾。
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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