FFPN: Fourier Feature Pyramid Network for Ultrasound Image Segmentation
Chaoyu Chen Xin Yang Rusi Chen Junxuan Yu Liwei Du Jian Wang Xindi Hu Yan Cao Yingying Liu Dong Ni
Ultrasound (US) image segmentation is an active research area that requires
real-time and highly accurate analysis in many scenarios. The detect-to-segment
(DTS) frameworks have been recently proposed to balance accuracy and
efficiency. However, existing approaches may suffer from inadequate contour
encoding or fail to effectively leverage the encoded results. In this paper, we
introduce a novel Fourier-anchor-based DTS framework called Fourier Feature
Pyramid Network (FFPN) to address the aforementioned issues. The contributions
of this paper are two fold. First, the FFPN utilizes Fourier Descriptors to
adequately encode contours. Specifically, it maps Fourier series with similar
amplitudes and frequencies into the same layer of the feature map, thereby
effectively utilizing the encoded Fourier information. Second, we propose a
Contour Sampling Refinement (CSR) module based on the contour proposals and
refined features produced by the FFPN. This module extracts rich features
around the predicted contours to further capture detailed information and
refine the contours. Extensive experimental results on three large and
challenging datasets demonstrate that our method outperforms other DTS methods
in terms of accuracy and efficiency. Furthermore, our framework can generalize
well to other detection or segmentation tasks.