School of Computer Science and Engineering, Dalian Minzu University, Dalian 116600, China.
Institute of Machine Intelligence and Biocomputing, Dalian Minzu University, Dalian 116600, China.
Math Biosci Eng. 2022 Mar 30;19(6):5576-5590. doi: 10.3934/mbe.2022261.
The precise segmentation of tumor regions plays a pivotal role in the diagnosis and treatment of brain tumors. However, due to the variable location, size, and shape of brain tumors, the automatic segmentation of brain tumors is a relatively challenging application. Recently, U-Net related methods, which largely improve the segmentation accuracy of brain tumors, have become the mainstream of this task. Following merits of the 3D U-Net architecture, this work constructs a novel 3D U-Net model called SGEResU-Net to segment brain tumors. SGEResU-Net simultaneously embeds residual blocks and spatial group-wise enhance (SGE) attention blocks into a single 3D U-Net architecture, in which SGE attention blocks are employed to enhance the feature learning of semantic regions and reduce possible noise and interference with almost no extra parameters. Besides, the self-ensemble module is also utilized to improve the segmentation accuracy of brain tumors. Evaluation experiments on the Brain Tumor Segmentation (BraTS) Challenge 2020 and 2021 benchmarks demonstrate the effectiveness of the proposed SGEResU-Net for this medical application. Moreover, it achieves DSC values of 83.31, 91.64 and 86.85%, as well as Hausdorff distances (95%) of 19.278, 5.945 and 7.567 for the enhancing tumor, whole tumor, and tumor core on BraTS 2021 dataset, respectively.
肿瘤区域的精确分割在脑肿瘤的诊断和治疗中起着关键作用。然而，由于脑肿瘤的位置、大小和形状的多变性，脑肿瘤的自动分割是一个相对具有挑战性的应用。最近，U-Net 相关方法在很大程度上提高了脑肿瘤的分割精度，已成为该任务的主流。本工作基于 3D U-Net 架构的优点，构建了一种名为 SGEResU-Net 的新型 3D U-Net 模型，用于分割脑肿瘤。SGEResU-Net 同时将残差块和空间分组增强（SGE）注意力块嵌入到单个 3D U-Net 架构中，其中 SGE 注意力块用于增强语义区域的特征学习，并减少可能的噪声和干扰，几乎不增加额外的参数。此外，还利用自集成模块来提高脑肿瘤的分割精度。在 Brain Tumor Segmentation (BraTS) Challenge 2020 和 2021 基准上的评估实验证明了所提出的 SGEResU-Net 对于该医学应用的有效性。此外，它在 BraTS 2021 数据集上分别实现了增强肿瘤、全肿瘤和肿瘤核心的 DSC 值为 83.31、91.64 和 86.85%，以及 Hausdorff 距离（95%）为 19.278、5.945 和 7.567。
