Malathi M, Sinthia P
Saveetha Engineering College,Chennai, India. Email:
Asian Pac J Cancer Prev. 2019 Jul 1;20(7):2095-2101. doi: 10.31557/APJCP.2019.20.7.2095.
Introduction: The determination of tumour extent is a major challenging task in brain tumour planning and quantitative evaluation. Magnetic Resonance Imaging (MRI) is one of the non-invasive technique has emanated as a front- line diagnostic tool for brain tumour without ionizing radiation. Objective: Among brain tumours, gliomas are the most common aggressive, leading to a very short life expectancy in their highest grade. In the clinical practice manual segmentation is a time consuming task and their performance is highly depended on the operator’s experience. Methods: This paper proposes fully automatic segmentation of brain tumour using convolutional neural network. Further, it uses high grade gilomas brain image from BRATS 2015 database. The suggested work accomplishes brain tumour segmentation using tensor flow, in which the anaconda frameworks are used to implement high level mathematical functions. The survival rates of patients are improved by early diagnosis of brain tumour. Results: Hence, the research work segments brain tumour into four classes like edema, non-enhancing tumour, enhancing tumour and necrotic tumour. Brain tumour segmentation needs to separate healthy tissues from tumour regions such as advancing tumour, necrotic core and surrounding edema. This is an essential step in diagnosis and treatment planning, both of which need to take place quickly in case of a malignancy in order to maximize the likelihood of successful treatment.
在脑肿瘤规划和定量评估中，确定肿瘤范围是一项重大挑战。磁共振成像（MRI）是一种无创技术，已成为用于脑肿瘤的一线诊断工具，且无需电离辐射。目的：在脑肿瘤中，胶质瘤是最常见的侵袭性肿瘤，最高级别时患者预期寿命极短。在临床实践中，手动分割耗时且其性能高度依赖操作者经验。方法：本文提出使用卷积神经网络对脑肿瘤进行全自动分割。此外，使用来自BRATS 2015数据库的高级别胶质瘤脑图像。所建议的工作使用张量流完成脑肿瘤分割，其中使用Anaconda框架来实现高级数学函数。脑肿瘤的早期诊断可提高患者生存率。结果：因此，该研究工作将脑肿瘤分为水肿、非增强肿瘤、增强肿瘤和坏死肿瘤四类。脑肿瘤分割需要将健康组织与肿瘤区域（如进展性肿瘤、坏死核心和周围水肿）分开。这是诊断和治疗规划中的关键步骤，对于恶性肿瘤情况，两者都需要迅速进行，以最大化成功治疗的可能性。
