Brain Tumor Segmentation (BraTS) plays a critical role in clinical diagnosis, treatment planning, and monitoring the progression of brain tumors. However, due to the variability in tumor appearance, size, and intensity across different MRI modalities, automated segmentation remains a challenging task. In this study, we propose a novel Transformer-based framework, multiPI-TransBTS, which integrates multi-physical information to enhance segmentation accuracy. The model leverages spatial information, semantic information, and multi-modal imaging data, addressing the inherent heterogeneity in brain tumor characteristics. The multiPI-TransBTS framework consists of an encoder, an Adaptive Feature Fusion (AFF) module, and a multi-source, multi-scale feature decoder. The encoder incorporates a multi-branch architecture to separately extract modality-specific features from different MRI sequences. The AFF module fuses information from multiple sources using channel-wise and element-wise attention, ensuring effective feature recalibration. The decoder combines both common and task-specific features through a Task-Specific Feature Introduction (TSFI) strategy, producing accurate segmentation outputs for Whole Tumor (WT), Tumor Core (TC), and Enhancing Tumor (ET) regions. Comprehensive evaluations on the BraTS2019 and BraTS2020 datasets demonstrate the superiority of multiPI-TransBTS over the state-of-the-art methods. The model consistently achieves better Dice coefficients, Hausdorff distances, and Sensitivity scores, highlighting its effectiveness in addressing the BraTS challenges. Our results also indicate the need for further exploration of the balance between precision and recall in the ET segmentation task. The proposed framework represents a significant advancement in BraTS, with potential implications for improving clinical outcomes for brain tumor patients.

翻译：脑肿瘤分割（BraTS）在临床诊断、治疗规划和脑肿瘤进展监测中起着至关重要的作用。然而，由于肿瘤在不同MRI模态下的外观、大小和强度存在差异，自动化分割仍然是一项具有挑战性的任务。在本研究中，我们提出了一种新颖的基于Transformer的框架——multiPI-TransBTS，该框架集成了多物理信息以提高分割精度。该模型利用空间信息、语义信息以及多模态成像数据，以应对脑肿瘤特征固有的异质性。multiPI-TransBTS框架由一个编码器、一个自适应特征融合（AFF）模块和一个多源多尺度特征解码器组成。编码器采用多分支架构，分别从不同的MRI序列中提取模态特异性特征。AFF模块通过通道注意力和元素级注意力融合来自多个源的信息，确保有效的特征重校准。解码器通过任务特异性特征引入（TSFI）策略，结合通用特征和任务特异性特征，为全肿瘤（WT）、肿瘤核心（TC）和增强肿瘤（ET）区域生成精确的分割输出。在BraTS2019和BraTS2020数据集上的综合评估表明，multiPI-TransBTS优于现有最先进的方法。该模型在Dice系数、Hausdorff距离和敏感性得分上持续取得更优结果，突显了其在应对BraTS挑战方面的有效性。我们的结果还表明，在ET分割任务中，需要进一步探索精确率与召回率之间的平衡。所提出的框架代表了BraTS领域的重要进展，对改善脑肿瘤患者的临床预后具有潜在意义。