Glioma brain tumour is one of the life‐threatening diseases in the world. Tumour substructure segmentation by physicians is a time‐consuming task with the magnetic resonance imaging (MRI) technique due to the size of clinical data. An automatic and well‐trained method is essential to detect and segment the tumour which increase the survival of the patients. The proposed work aims to produce high accuracy on glioma substructures segmentation with less computation time using deep learning. From the literature survey, the following challenges are found: (i) computing complex spatial boundaries between normal and tumour tissues, (ii) feature reduction and (iii) overfitting problems. Hence, we proposed a fully automatic glioma tumour segmentation using a residual‐inception block (RIB) with a modified 3D U‐Net (RIBM3DU‐Net). It includes three phases: pre‐processing, modified 3D U‐Net segmentation and post‐processing. From the results, RIB with U‐Net enhances the segmentation accuracy. GPU parallel architecture reduces the computation time while training and testing. For quantitative analysis, comprehensive experiment results were computed and compared with state‐of‐the‐art methods. It achieves better Dice scores on enhancing tumour, tumour core and complete tumour of 87%, 87% and 94%, respectively. GPU speedup folds yield up to 48× when compared with CPU. Quantitatively, 3D glioma volume is rendered from the obtained segmented results and estimated using the Cavalieri estimator.

中文翻译：

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RIBM3DU-Net：使用带有改进的 3D U-Net 架构的残差起始块对磁共振图像中的胶质瘤肿瘤子结构进行分割

胶质瘤脑肿瘤是世界上危及生命的疾病之一。由于临床数据的规模，医生使用磁共振成像（MRI）技术进行肿瘤亚结构分割是一项耗时的任务。训练有素的自动方法对于检测和分割肿瘤至关重要，这可以提高患者的生存率。所提出的工作旨在利用深度学习以更少的计算时间实现神经胶质瘤子结构分割的高精度。从文献调查中，发现了以下挑战：（i）计算正常组织和肿瘤组织之间的复杂空间边界，（ii）特征减少和（iii）过度拟合问题。因此，我们提出了使用带有改进的 3D U-Net (RIBM3DU-Net) 的残余起始块 (RIB) 的全自动神经胶质瘤肿瘤分割。它包括三个阶段：预处理、修改的 3D U-Net 分割和后处理。从结果来看，带有 U-Net 的 RIB 提高了分割精度。 GPU并行架构减少了训练和测试时的计算时间。对于定量分析，计算了综合实验结果并与最先进的方法进行比较。它在增强肿瘤、肿瘤核心和完整肿瘤方面取得了更好的 Dice 评分，分别为 87%、87% 和 94%。与 CPU 相比，GPU 加速倍数高达 48 倍。从获得的分割结果中定量地呈现 3D 神经胶质瘤体积，并使用 Cavalieri 估计器进行估计。