Brain Tumour Image Segmentation Using Deep Networks
Brain Tumour Image Segmentation Using Deep Networks
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DOI：
10.1109/access.2020.3018160
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Jamil, Mohsin
中科院分区：
文献类型：
--
作者：
Ali, Mahnoor;Gilani, Syed Omer;Jamil, Mohsin
从多模态MR图像中自动分割脑肿瘤对于分析和监测疾病进展至关重要。由于神经胶质瘤是恶性和异质性的，高效和准确的分割技术用于成功地将肿瘤划分为肿瘤内类别。深度学习算法在语义分割任务上优于更传统的基于上下文的计算机视觉方法。卷积神经网络广泛用于生物医学图像分割，显著提高了脑肿瘤分割任务的最新精度。在本文中，我们提出了两个分割网络的集成：3D CNN和U-Net，这是一种重要而直接的组合技术，可以实现更好和更准确的预测。这两个模型分别在BraTS-19挑战数据集上进行训练，并进行评估，以产生在分割的肿瘤子区域方面彼此差异很大的分割图，并进行集成以实现最终预测。建议的合奏达到骰子得分为0.750，0.906和0.846，分别为增强肿瘤，整个肿瘤，和肿瘤核心，验证集，表现良好，目前可用的最先进的架构相比。
Automated segmentation of brain tumour from multimodal MR images is pivotal for the analysis and monitoring of disease progression. As gliomas are malignant and heterogeneous, efficient and accurate segmentation techniques are used for the successful delineation of tumours into intra-tumoural classes. Deep learning algorithms outperform on tasks of semantic segmentation as opposed to the more conventional, context-based computer vision approaches. Extensively used for biomedical image segmentation, Convolutional Neural Networks have significantly improved the state-of-the-art accuracy on the task of brain tumour segmentation. In this paper, we propose an ensemble of two segmentation networks: a 3D CNN and a U-Net, in a significant yet straightforward combinative technique that results in better and accurate predictions. Both models were trained separately on the BraTS-19 challenge dataset and evaluated to yield segmentation maps which considerably differed from each other in terms of segmented tumour sub-regions and were ensembled variably to achieve the final prediction. The suggested ensemble achieved dice scores of 0.750, 0.906 and 0.846 for enhancing tumour, whole tumour, and tumour core, respectively, on the validation set, performing favourably in comparison to the state-of-the-art architectures currently available.