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.

本文提出了一种浅层编码器和解码器网络（SEDNet）用于脑肿瘤分割，结合预处理算法和优化函数，通过对BraTS2020数据集的测试取得了显著的性能，并通过迁移学习进一步提高了分割准确度，因此，在实时临床诊断中表现出了高效性。

SEDNet: 浅层编码器-解码器网络用于脑肿瘤分割