Weighted Monte Carlo augmented spherical Fourier-Bessel convolutional layers for 3D abdominal organ segmentation
CoRR(2024)
摘要
Filter-decomposition-based group equivariant convolutional neural networks
show promising stability and data efficiency for 3D image feature extraction.
However, the existing filter-decomposition-based 3D group equivariant neural
networks rely on parameter-sharing designs and are mostly limited to rotation
transform groups, where the chosen spherical harmonic filter bases consider
only angular orthogonality. These limitations hamper its application to deep
neural network architectures for medical image segmentation. To address these
issues, this paper describes a non-parameter-sharing affine group equivariant
neural network for 3D medical image segmentation based on an adaptive
aggregation of Monte Carlo augmented spherical Fourier Bessel filter bases. The
efficiency and flexibility of the adopted non-parameter strategy enable for the
first time an efficient implementation of 3D affine group equivariant
convolutional neural networks for volumetric data. The introduced spherical
Bessel Fourier filter basis combines both angular and radial orthogonality for
better feature extraction. The 3D image segmentation experiments on two
abdominal image sets, BTCV and the NIH Pancreas datasets, show that the
proposed methods excel the state-of-the-art 3D neural networks with high
training stability and data efficiency. The code will be available at
https://github.com/ZhaoWenzhao/WVMS.
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