面向脑肿瘤分割的生物物理驱动病理正则化方法
Biophysics Informed Pathological Regularisation for Brain Tumour Segmentation
AI总结：
针对现有脑肿瘤分割方法因缺乏生物物理先验导致鲁棒性不足的问题，提出将肿瘤生长PDE模型作为深度学习正则项的新方法，在BraTS 2023数据集上验证了其在精度与可靠性上的提升，尤其适用于数据稀缺场景。
摘要：
深度学习的最新进展显著提升了脑肿瘤分割技术的效果，但由于这类方法仅考虑图像数据，未引入生物物理先验或病理信息，其结果仍缺乏置信度与鲁棒性。融入生物物理驱动的正则化是改善这一现状的有效途径，可为端到端自动学习提供先验正则约束。本文提出一种新方法，将脑肿瘤生长偏微分方程（Partial Differential Equation，PDE）模型设计为深度学习的正则项，可适配任意网络模型。该方法将肿瘤生长PDE模型直接引入分割流程，尤其在数据稀缺场景下能提升分割的准确性与鲁棒性。系统通过周期激活函数估计肿瘤细胞密度，将该估计结果与生物物理模型有效融合，可更好地捕捉肿瘤特征。该方法不仅让分割结果更贴合真实生物行为，还增强了模型在数据有限条件下的性能。我们在BraTS 2023数据集上开展了大量实验，验证了框架的有效性，结果显示肿瘤分割的精度和可靠性均得到显著提升。
英文摘要：
Recent advances in deep learning have significantly improved brain tumour segmentation techniques; however, the results still lack confidence and robustness as they solely consider image data without biophysical priors or pathological information. Integrating biophysics-informed regularisation is one effective way to change this situation, as it provides an prior regularisation for automated end-to-end learning. In this paper, we propose a novel approach that designs brain tumour growth Partial Differential Equation (PDE) models as a regularisation with deep learning, operational with any network model. Our method introduces tumour growth PDE models directly into the segmentation process, improving accuracy and robustness, especially in data-scarce scenarios. This system estimates tumour cell density using a periodic activation function. By effectively integrating this estimation with biophysical models, we achieve better capture of tumour characteristics. This approach not only aligns the segmentation closer to actual biological behaviour but also strengthens the model's performance under limited data conditions. We demonstrate the effectiveness of our framework through extensive experiments on the BraTS 2023 dataset, showcasing significant improvements in both precision and reliability of tumour segmentation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
