PURPOSE Gliomas are the most common and aggressive type of brain tumors due to their infiltrative nature and rapid progression. The process of distinguishing tumor boundaries from healthy cells is still a challenging task in the clinical routine. Fluid-attenuated inversion recovery (FLAIR) MRI modality can provide the physician with information about tumor infiltration. Therefore, this paper proposes a new generic deep learning architecture, namely DeepSeg, for fully automated detection and segmentation of the brain lesion using FLAIR MRI data. METHODS The developed DeepSeg is a modular decoupling framework. It consists of two connected core parts based on an encoding and decoding relationship. The encoder part is a convolutional neural network (CNN) responsible for spatial information extraction. The resulting semantic map is inserted into the decoder part to get the full-resolution probability map. Based on modified U-Net architecture, different CNN models such as residual neural network (ResNet), dense convolutional network (DenseNet), and NASNet have been utilized in this study. RESULTS The proposed deep learning architectures have been successfully tested and evaluated on-line based on MRI datasets of brain tumor segmentation (BraTS 2019) challenge, including s336 cases as training data and 125 cases for validation data. The dice and Hausdorff distance scores of obtained segmentation results are about 0.81 to 0.84 and 9.8 to 19.7 correspondingly. CONCLUSION This study showed successful feasibility and comparative performance of applying different deep learning models in a new DeepSeg framework for automated brain tumor segmentation in FLAIR MR images. The proposed DeepSeg is open source and freely available at https://github.com/razeineldin/DeepSeg/.

中文翻译：

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DeepSeg：使用磁共振FLAIR图像进行脑肿瘤自动分割的深度神经网络框架。

目的脑胶质瘤由于其浸润性和快速发展而成为最常见和侵袭性的脑肿瘤。在临床常规中，区分肿瘤边界与健康细胞的过程仍然是一项艰巨的任务。液衰减倒置恢复（FLAIR）MRI方式可为医生提供有关肿瘤浸润的信息。因此，本文提出了一种新的通用深度学习架构，即DeepSeg，该架构可使用FLAIR MRI数据全自动检测和分割脑部病变。方法开发的DeepSeg是一个模块化的去耦框架。它由基于编码和解码关系的两个相连的核心部分组成。编码器部分是负责空间信息提取的卷积神经网络（CNN）。将生成的语义图插入解码器部分，以获得全分辨率概率图。在改进的U-Net体系结构的基础上，本研究利用了残差神经网络（ResNet），密集卷积网络（DenseNet）和NASNet等不同的CNN模型。结果基于脑肿瘤分割（BraTS 2019）挑战的MRI数据集，已成功在线测试和评估了所提议的深度学习架构，其中包括s336例训练数据和125例验证数据。所获得的分割结果的骰子和Hausdorff距离得分分别约为0.81至0.84和9.8至19.7。结论这项研究显示了在不同的DeepSeg框架中将不同的深度学习模型应用于FLAIR MR图像中自动脑肿瘤分割的成功可行性和比较性能。拟议的DeepSeg是开源的，可以从https://github.com/razeineldin/DeepSeg/免费获得。