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multiPI-TransBTS: A Multi-Path Learning Framework for Brain Tumor Image Segmentation Based on Multi-Physical Information

2024-09-18 17:35:19
Hongjun Zhu, Jiaohang Huang, Kuo Chen, Xuehui Ying, Ying Qian

Abstract

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.

Abstract (translated)

Brain Tumor Segmentation (BraTS) 在临床诊断、治疗规划和监测肿瘤进展方面起着关键作用。然而,由于不同MRI模态肿瘤外观、大小和强度的变异性,自动分割仍然具有挑战性。在这项研究中,我们提出了一个新型的Transformer-基框架,多PI-TransBTS,整合了多物理信息以提高分割准确性。该模型利用空间信息、语义信息和多模态成像数据,解决肿瘤特征固有的异质性。多PI-TransBTS框架包括编码器、自适应特征融合模块和多源多尺度特征解码器。编码器采用多分支架构,分别从不同MRI序列中提取模块特定的特征。自适应特征融合模块利用通道级和元素级注意将多个来源的信息进行有效融合,确保精确的特征重调。解码器通过任务特定特征引入(TSFI)策略将常见和任务特定的特征结合,产生准确的全肿瘤(WT)、肿瘤核心(TC)和增强肿瘤(ET)区域的分割输出。对BraTS2019和BraTS2020数据集的全面评估表明,多PI-TransBTS相对于最先进的方法具有优越性。模型在Dice系数、汉明距离和敏感分数方面始终优于其他方法,突出了其在解决BraTS挑战方面的重要性。我们的结果还表明,在ET分割任务中需要进一步研究精度和召回之间的平衡。所提出的框架在 BraTS 研究中取得了显著的进展,对改善脑肿瘤患者的临床治疗效果具有潜在影响。

URL

https://arxiv.org/abs/2409.12167

PDF

https://arxiv.org/pdf/2409.12167.pdf


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