Biophysics Informed Pathological Regularisation for Brain Tumour Segmentation
CoRR(2024)
摘要
Recent advancements 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 a 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.
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