论文标题
从多MR序列通过深卷积神经网络分割关节脑肿瘤
Joint brain tumor segmentation from multi MR sequences through a deep convolutional neural network
论文作者
论文摘要
脑肿瘤细分在诊断和治疗计划方面有很大贡献。手动脑肿瘤描述是一项耗时且繁琐的任务，并且取决于放射科医生的技能。自动化的脑肿瘤分割非常重要，并且不依赖于间或观察内的。这项研究的目的是通过深度学习方法从天赋，T1加权和T1加权对比度增强的MR序列中自动化脑肿瘤的描述，重点是确定单独使用哪种MR序列或其中哪种组合将导致其中最高精度。
Brain tumor segmentation is highly contributive in diagnosing and treatment planning. The manual brain tumor delineation is a time-consuming and tedious task and varies depending on the radiologists skill. Automated brain tumor segmentation is of high importance, and does not depend on either inter or intra-observation. The objective of this study is to automate the delineation of brain tumors from the FLAIR, T1 weighted, T2 weighted, and T1 weighted contrast-enhanced MR sequences through a deep learning approach, with a focus on determining which MR sequence alone or which combination thereof would lead to the highest accuracy therein.
