Despite the advancement in computational modeling towards brain tumor segmentation, of which several models have been developed, it is evident from the computational complexity of existing models which are still at an all-time high, that performance and efficiency under clinical application scenarios are limited. Therefore, this paper proposes a shallow encoder and decoder network named SEDNet for brain tumor segmentation. The proposed network is adapted from the U-Net structure. Though brain tumors do not assume complex structures like the task the traditional U-Net was designed for, their variance in appearance, shape, and ambiguity of boundaries makes it a compelling complex task to solve. SEDNet architecture design is inspired by the localized nature of brain tumors in brain images, thus consists of sufficient hierarchical convolutional blocks in the encoding pathway capable of learning the intrinsic features of brain tumors in brain slices, and a decoding pathway with selective skip path sufficient for capturing miniature local-level spatial features alongside the global-level features of brain tumor. SEDNet with the integration of the proposed preprocessing algorithm and optimization function on the BraTS2020 set reserved for testing achieves impressive dice and Hausdorff scores of 0.9308, 0.9451, 0.9026, and 0.7040, 1.2866, 0.7762 for non-enhancing tumor core (NTC), peritumoral edema (ED), and enhancing tumor (ET), respectively. Furthermore, through transfer learning with initialized SEDNet pre-trained weights, termed SEDNetX, a performance increase is observed. The dice and Hausdorff scores recorded are 0.9336, 0.9478, 0.9061, 0.6983, 1.2691, and 0.7711 for NTC, ED, and ET, respectively. With about 1.3 million parameters and impressive performance in comparison to the state-of-the-art, SEDNet(X) is shown to be computationally efficient for real-time clinical diagnosis.

翻译：尽管在脑肿瘤分割的计算建模方面取得了进展，并开发了多种模型，但现有模型的计算复杂度仍处于历史高位，这表明其在临床应用场景下的性能和效率依然有限。因此，本文提出一种名为SEDNet的浅层编码器-解码器网络，用于脑肿瘤分割。该网络基于U-Net结构改进而来。尽管脑肿瘤的复杂程度不及传统U-Net所针对的任务，但其外观、形状的变异以及边界的模糊性，使其成为一个极具挑战性的复杂任务。SEDNet架构设计受脑肿瘤在脑图像中的局部化特性启发，其编码路径包含足够的分层卷积模块，能够学习脑切片中脑肿瘤的本质特征；解码路径则结合了选择性跳跃连接，可同时捕捉脑肿瘤的微型局部空间特征与全局特征。通过集成所提出的预处理算法与优化函数，SEDNet在BraTS2020测试集上取得了优异的Dice系数和Hausdorff距离分数：对于非增强肿瘤核心（NTC）、瘤周水肿（ED）和增强肿瘤（ET），分数分别为0.9308、0.9451、0.9026和0.7040、1.2866、0.7762。此外，通过迁移学习初始化SEDNet预训练权重（称为SEDNetX）后，性能进一步提升：NTC、ED和ET的Dice系数和Hausdorff距离分数分别达到0.9336、0.9478、0.9061和0.6983、1.2691、0.7711。SEDNet(X)参数约130万，在与现有最优方法相比表现优异的同时，展示了其在实时临床诊断中的计算效率。