In medical imaging, extraction of brain tumor region in the magnetic resonance image (MRI) is not sufficient, but finding the tumor extension is necessary to plan best treatment to improve the survival rate as it depends on tumor’s size, location, and patient’s age. Manually extracting the brain tumor sub-regions from MRI volume is tedious, time consuming and the inherently complex brain tumor images requires a proficient radiologist. Thus, a reliable multi-modal deep learning models are proposed for automatic segmentation to extract the sub-regions like enhancing tumor (ET), tumor core (TC), and whole tumor (WT). These models are constructed on the basis of U-net and VGG16 architectures. The whole tumor is obtained by segmenting T2-weighted images and cross-check the edema’s extension in T2 fluid attenuated inversion recovery (FLAIR). ET and TC are both extracted by evaluating the hyper-intensities in T1-weighted contrast enhanced images. The proposed method has produced better results in terms of dice similarity index, Jaccard similarity index, accuracy, specificity, and sensitivity for segmented sub regions. The experimental results on BraTS 2018 database shows the proposed DL model outperforms with average dice coefficients of 0.91521, 0.92811, 0.96702, and Jaccard coefficients of 0.84715, 0.88357, 0.93741 for ET, TC, and WT respectively.

中文翻译：

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使用深度学习分割多模式 MRI 脑肿瘤子区域

在医学成像中，仅提取磁共振图像 (MRI) 中的脑肿瘤区域是不够的，但由于肿瘤的大小、位置和患者年龄不同，因此需要找到肿瘤的扩展区域以制定最佳治疗方案以提高生存率。从 MRI 体积中手动提取脑肿瘤子区域是繁琐、耗时的，并且固有复杂的脑肿瘤图像需要熟练的放射科医生。因此，提出了一种可靠的多模态深度学习模型，用于自动分割以提取子区域，如增强肿瘤（ET）、肿瘤核心（TC）和整个肿瘤（WT）。这些模型是在 U-net 和 VGG16 架构的基础上构建的。通过分割 T2 加权图像并交叉检查 T2 液体衰减反转恢复 (FLAIR) 中水肿的扩展，获得整个肿瘤。ET 和 TC 都是通过评估 T1 加权对比度增强图像中的超强度来提取的。所提出的方法在分割子区域的骰子相似度指数、Jaccard相似度指数、准确性、特异性和敏感性方面产生了更好的结果。BraTS 2018 数据库上的实验结果表明，所提出的 DL 模型优于 ET、TC 和 WT 的平均骰子系数为 0.91521、0.92811、0.96702 和 Jaccard 系数为 0.84715、0.88357、0.93741