FFPN: Fourier Feature Pyramid Network for Ultrasound Image Segmentation

TL;DR

This paper introduces FFPN, a Fourier-based detect-to-segment framework that improves ultrasound image segmentation accuracy and efficiency by effectively encoding contours and refining predictions.

Contribution

The paper presents a novel Fourier Descriptor-based contour encoding and a contour refinement module, enhancing segmentation performance over existing methods.

Findings

Outperforms existing DTS methods in accuracy and efficiency

Effectively encodes contours using Fourier Descriptors

Generalizes well to other detection and segmentation tasks

Abstract

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.