mlsanityData to 7 Oct 2026

MasHeNe: A Benchmark for Head and Neck CT Mass Segmentation using Window-Enhanced Mamba with Frequency-Domain Integration

arXiv 2512.01563 ↗First posted 1 Dec 2025cs.CV cs.AIcited by 0

On the map: State-space sequence models (S4, Mamba) → Mamba for medical image analysis

Abstract (arXiv)

Head and neck masses are space-occupying lesions that can compress the airway and esophagus and may affect nerves and blood vessels. Available public datasets primarily focus on malignant lesions and often overlook other space-occupying conditions in this region. To address this gap, we introduce MasHeNe, an initial dataset of 3,779 contrast-enhanced CT slices that includes both tumors and cysts with pixel-level annotations. We also establish a benchmark using standard segmentation baselines and report common metrics to enable fair comparison. In addition, we propose the Windowing-Enhanced Mamba with Frequency integration (WEMF) model. WEMF applies tri-window enhancement to enrich the input appearance before feature extraction. It further uses multi-frequency attention to fuse information across skip connections within a U-shaped Mamba backbone. On MasHeNe, WEMF attains the best performance among evaluated methods, with a Dice of 70.45%, IoU of 66.89%, NSD of 72.33%, and HD95 of 5.12 mm. This model indicates stable and strong results on this challenging task. MasHeNe provides a benchmark for head-and-neck mass segmentation beyond malignancy-only datasets. The observed error patterns also suggest that this task remains challenging and requires further research. Our dataset and code are available at https://github.com/drthaodao3101/MasHeNe.git.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 18 of its 40 references that are in the map. First posted on arXiv 1 Dec 2025.

Builds on

18 of 40 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining5 Feb
  2. 2024VM-UNet: Vision Mamba UNet for Medical Image Segmentation4 Feb
  3. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation9 Jan

Other references · 15

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  4. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  5. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  6. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  7. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  8. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  9. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
  10. 2014Deeply-Supervised Netscited by 364
  11. 2018Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentationcited by 180
  12. 2017Radiomics strategies for risk assessment of tumour failure in head-and-neck cancercited by 31
3 more
  1. 2023SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinomacited by 28
  2. 2019Stochastic tissue window normalization of deep learning on computed tomographycited by 4
  3. 2023View it like a radiologist: Shifted windows for deep learning augmentation of CT imagescited by 3

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Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Toward Content-based Indexing and Retrieval of Head and Neck CT with Abscess Segmentation
  2. 2026MHMamba: Multi-Head Mamba for 3D Brain Tumor Segmentation
  3. 2024S3-Mamba: Small-Size-Sensitive Mamba for Lesion Segmentation
  4. 2025HybridMamba: A Dual-domain Mamba for 3D Medical Image Segmentation
  5. 2025DRBD-Mamba for Robust and Efficient Brain Tumor Segmentation with Analytical Insights
  6. 2025Topology-Aware Wavelet Mamba for Airway Structure Segmentation in Postoperative Recurrent Nasopharyngeal Carcinoma CT Scans
  7. 2024MLLA-UNet: Mamba-like Linear Attention in an Efficient U-Shape Model for Medical Image Segmentation
  8. 2024MambaClinix: Hierarchical Gated Convolution and Mamba-Based U-Net for Enhanced 3D Medical Image Segmentation