LiteFreqMamba:lightweight frequency-enhanced Mamba for efficient 3D brain tumor segmentation.

Overview

1. The Engineering & Technical College of Chengdu University of Technology, Leshan, China

Abstract

Introduction: State Space Models (SSMs) have demonstrated strong potential for 3D brain tumor segmentation owing to their linear computational complexity. However, conventional Mamba-based models are often limited by spectral bias, which favors low-frequency information while overlooking high-frequency boundary details, as well as by spatial disruption introduced by 1D scanning.

Methods: We propose LiteFreqMamba, a novel frequency-enhanced architecture for accurate and efficient 3D brain tumor segmentation. LiteFreqMamba is designed to improve boundary representation and spatial modeling through several key components. First, a Decomposed Frequency-Spatial Convolution (DFS-Conv) encoder is introduced to explicitly decompose features and capture high-frequency boundary information in shallow layers, thereby alleviating boundary ambiguity. Second, a Mamba-Attention Hybrid Bottleneck (MAHB) is developed to preserve spatial structure by combining the 2D-Selective-Scan (SS2D) mechanism for linear-complexity spatial mixing with dense self-attention for finegrained pixel-wise dependency modeling. In addition, Frequency-Calibrated Skip Connections (FCSC) are proposed to dynamically suppress noise in high-frequency feature injection, and a Lightweight 3D Convolutional (LWT-3D Conv) decoder is employed for efficient feature reconstruction.

Results: Extensive experiments on the BraTS2020 and BraTS2021 benchmarks show that LiteFreqMamba surpasses existing efficient segmentation models and achieves a better balance between inference speed and segmentation accuracy.

Discussion: LiteFreqMamba is designed to improve high-frequency boundary representation and preserve spatial dependencies in Mamba-based architectures. The proposed framework provides an efficient and accurate solution for 3D brain tumor image segmentation.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/dschettler8845, at Kaggle; found in “Data availability statement”

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: The datasets used in this study is publicly available from Multimodal Brain Tumor Segmentation Challenge. BraTS2020: https://www.med.upenn.edu/cbica/brats2020/data.html; BraTS2021: https://www.kaggle.com/datasets/dschettler8845/brats-2021-task1. All experiments were conducted solely on this dataset and strictly adhered to the competition’s data usage terms.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 17 references.

Cite

This paper

Hou, X., Yao, J., Li, Q., Xu, C., & Dong, W. (2026). LiteFreqMamba:lightweight frequency-enhanced Mamba for efficient 3D brain tumor segmentation. Frontiers in oncology, 16, 1928589. https://doi.org/10.3389/fonc.2026.1928589

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