FRCNet Frequency and Region Consistency for Semi-supervised Medical Image Segmentation

TL;DR

This paper introduces FRCNet, a semi-supervised medical image segmentation method that leverages frequency and region consistency strategies to improve feature learning and achieve superior performance on benchmark datasets.

Contribution

It proposes two novel consistency regularization strategies, frequency domain consistency and multi-granularity region similarity, to enhance semi-supervised segmentation.

Findings

Significant performance improvements over state-of-the-art methods.

Effective utilization of unlabeled data through frequency and region consistency.

Robust segmentation results across multiple datasets.

Abstract

Limited labeled data hinder the application of deep learning in medical domain. In clinical practice, there are sufficient unlabeled data that are not effectively used, and semi-supervised learning (SSL) is a promising way for leveraging these unlabeled data. However, existing SSL methods ignore frequency domain and region-level information and it is important for lesion regions located at low frequencies and with significant scale changes. In this paper, we introduce two consistency regularization strategies for semi-supervised medical image segmentation, including frequency domain consistency (FDC) to assist the feature learning in frequency domain and multi-granularity region similarity consistency (MRSC) to perform multi-scale region-level local context information feature learning. With the help of the proposed FDC and MRSC, we can leverage the powerful feature representation capability of them in an effective and efficient way. We perform comprehensive experiments on two datasets, and the results show that our method achieves large performance gains and exceeds other state-of-the-art methods.