Accurate segmentation of glioma brain tumors is crucial for diagnosis and treatment planning. Deep learning techniques offer promising solutions, but optimal model architectures remain under investigation. We used the BraTS 2021 dataset, selecting T1 with contrast enhancement (T1CE), T2, and Fluid-Attenuated Inversion Recovery (FLAIR) sequences for model development. The proposed Attention Xception UNet (AXUNet) architecture integrates an Xception backbone with dot-product self-attention modules, inspired by state-of-the-art (SOTA) large language models such as Google Bard and OpenAI ChatGPT, within a UNet-shaped model. We compared AXUNet with SOTA models. Comparative evaluation on the test set demonstrated improved results over baseline models. Inception-UNet and Xception-UNet achieved mean Dice scores of 90.88 and 93.24, respectively. Attention ResUNet (AResUNet) attained a mean Dice score of 92.80, with the highest score of 84.92 for enhancing tumor (ET) among all models. Attention Gate UNet (AGUNet) yielded a mean Dice score of 90.38. AXUNet outperformed all models with a mean Dice score of 93.73. It demonstrated superior Dice scores across whole tumor (WT) and tumor core (TC) regions, achieving 92.59 for WT, 86.81 for TC, and 84.89 for ET. The integration of the Xception backbone and dot-product self-attention mechanisms in AXUNet showcases enhanced performance in capturing spatial and contextual information. The findings underscore the potential utility of AXUNet in facilitating precise tumor delineation.

翻译：胶质瘤脑肿瘤的精确分割对于诊断和治疗规划至关重要。深度学习技术提供了有前景的解决方案，但最优的模型架构仍在探索中。我们使用BraTS 2021数据集，选择T1对比增强（T1CE）、T2和液体衰减反转恢复（FLAIR）序列进行模型开发。所提出的注意力Xception UNet（AXUNet）架构将Xception主干网络与点积自注意力模块相结合，其灵感来源于最先进的大型语言模型（如Google Bard和OpenAI ChatGPT），并集成于UNet形状的模型中。我们将AXUNet与最先进的模型进行了比较。在测试集上的对比评估表明，其结果优于基线模型。Inception-UNet和Xception-UNet分别取得了90.88和93.24的平均Dice分数。注意力ResUNet（AResUNet）获得了92.80的平均Dice分数，并在所有模型中取得了增强肿瘤（ET）的最高分数84.92。注意力门控UNet（AGUNet）的平均Dice分数为90.38。AXUNet以93.73的平均Dice分数优于所有模型。它在全肿瘤（WT）和肿瘤核心（TC）区域均表现出优异的Dice分数，WT为92.59，TC为86.81，ET为84.89。AXUNet中Xception主干网络与点积自注意力机制的集成，展示了其在捕获空间和上下文信息方面增强的性能。这些发现强调了AXUNet在促进精确肿瘤勾画方面的潜在效用。