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 that performance and efficiency under clinical application scenarios are still limited. Therefore, this paper proposes a tumor segmentation framework. It includes a novel shallow encoder and decoder network named SEDNet for brain tumor segmentation. The highlights of SEDNet include sufficiency in hierarchical convolutional downsampling and selective skip mechanism for cost-efficient and effective brain tumor semantic segmentation, among other features. The preprocessor and optimization function approaches are devised to minimize the uncertainty in feature learning impacted by nontumor slices or empty masks with corresponding brain slices and address class imbalances as well as boundary irregularities of tumors, respectively. Through experiments, SEDNet achieved impressive dice and Hausdorff scores of 0.9308 %, 0.9451 %, and 0.9026 %, and 0.7040 mm, 1.2866 mm, and 0.7762 mm for the non-enhancing tumor core (NTC), peritumoral edema (ED), and enhancing tumor (ET), respectively. This is one of the few works to report segmentation performance on NTC. 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 mm, 1.2691 mm, and 0.7711 mm 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. The code is available on Github .

翻译：尽管脑肿瘤分割的计算建模已取得进展，并开发出多种模型，但从现有模型的计算复杂度来看，其在临床应用场景下的性能与效率仍存在局限。为此，本文提出一种肿瘤分割框架，其中包含一种名为SEDNet的新型浅层编码器-解码器网络，用于脑肿瘤分割。SEDNet的核心优势包括：通过分层卷积下采样与选择性跳跃机制，在保证成本效益的同时实现高效的脑肿瘤语义分割。研究设计了预处理与优化函数方法，分别用于最小化非肿瘤切片或空掩码对特征学习带来的不确定性影响，并解决类别不平衡及肿瘤边界不规则问题。实验表明，SEDNet在非增强肿瘤核心（NTC）、瘤周水肿（ED）和增强肿瘤（ET）区域分别取得了0.9308%、0.9451%、0.9026%的Dice分数，以及0.7040毫米、1.2866毫米、0.7762毫米的豪斯多夫距离分数。这是少数针对NTC区域报告分割性能的研究之一。进一步地，通过采用预训练权重初始化的迁移学习方法（称为SEDNetX），模型性能得到提升：NTC、ED、ET区域的Dice分数分别为0.9336%、0.9478%、0.9061%，豪斯多夫距离分数分别为0.6983毫米、1.2691毫米、0.7711毫米。SEDNet(X)仅需约130万参数，在保持优异性能的同时显著优于现有先进模型，展现出适用于实时临床诊断的高计算效率。相关代码已发布于Github平台。