Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Dec 26;25(1):81.
doi: 10.3390/s25010081.

Data-Efficient Bone Segmentation Using Feature Pyramid- Based SegFormer

Naohiro Masuda  1 , Keiko Ono  2 , Daisuke Tawara  3 , Yusuke Matsuura  4 , Kentaro Sakabe  1
Affiliations

Data-Efficient Bone Segmentation Using Feature Pyramid- Based SegFormer

Naohiro Masuda et al. Sensors (Basel). .

Abstract

The semantic segmentation of bone structures demands pixel-level classification accuracy to create reliable bone models for diagnosis. While Convolutional Neural Networks (CNNs) are commonly used for segmentation, they often struggle with complex shapes due to their focus on texture features and limited ability to incorporate positional information. As orthopedic surgery increasingly requires precise automatic diagnosis, we explored SegFormer, an enhanced Vision Transformer model that better handles spatial awareness in segmentation tasks. However, SegFormer's effectiveness is typically limited by its need for extensive training data, which is particularly challenging in medical imaging, where obtaining labeled ground truths (GTs) is a costly and resource-intensive process. In this paper, we propose two models and their combination to enable accurate feature extraction from smaller datasets by improving SegFormer. Specifically, these include the data-efficient model, which deepens the hierarchical encoder by adding convolution layers to transformer blocks and increases feature map resolution within transformer blocks, and the FPN-based model, which enhances the decoder through a Feature Pyramid Network (FPN) and attention mechanisms. Testing our model on spine images from the Cancer Imaging Archive and our own hand and wrist dataset, ablation studies confirmed that our modifications outperform the original SegFormer, U-Net, and Mask2Former. These enhancements enable better image feature extraction and more precise object contour detection, which is particularly beneficial for medical imaging applications with limited training data.

Keywords: Mask2Former; SegFormer; feature pyramid network; semantic segmentation; transformer block.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure A1
Figure A1
Datasets.
Figure A1
Figure A1
Datasets.
Figure A2
Figure A2
Spine segmentation.
Figure A2
Figure A2
Spine segmentation.
Figure A3
Figure A3
Hand and wrist segmentation.
Figure A3
Figure A3
Hand and wrist segmentation.
Figure A4
Figure A4
Femur segmantation.
Figure A4
Figure A4
Femur segmantation.
Figure 1
Figure 1
SegFormer architecture.
Figure 2
Figure 2
Proposed model architecture.
Figure 3
Figure 3
Data-efficient encoder architecture.
Figure 4
Figure 4
Model-wise IoU for spine images.
Figure 5
Figure 5
Model-wise IoU for hand and Wrist images.
Figure 6
Figure 6
Model-wise IoU for femur images.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Long J., Shelhamer E., Darrell T. Fully Convolutional Networks for Semantic Segmentation; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Boston, MA, USA. 7–12 June 2015; pp. 3431–3440.
    1. Badrinarayanan V., Kendall A., Cipolla R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2017;39:2481–2495. doi: 10.1109/TPAMI.2016.2644615. - DOI - PubMed
    1. Ronneberger O., Fischer P., Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation; Proceedings of the Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015; Munich, Germany. 5–9 October 2015; pp. 234–241.
    1. Petit O., Thome N., Rambour C., Soler L. U-Net Transformer: Self and Cross Attention for Medical Image Segmentation. arXiv. 20212103.06104
    1. Fabian I., Jaeger P.F., Kohl S.A.A., Petersen J., Maier-Hein K.H. nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods. 2021;18:203–211. - PubMed

MeSH terms

Grants and funding

LinkOut - more resources