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Link to original content: https://doi.org/10.1007/s11548-022-02607-1
Deep learning-based framework for segmentation of multiclass rib fractures in CT utilizing a multi-angle projection network | International Journal of Computer Assisted Radiology and Surgery Skip to main content

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Deep learning-based framework for segmentation of multiclass rib fractures in CT utilizing a multi-angle projection network

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Abstract

Purpose

Clinical rib fracture diagnosis via computed tomography (CT) screening has attracted much attention in recent years. However, automated and accurate segmentation solutions remain a challenging task due to the large sets of 3D CT data to deal with. Down-sampling is often required to face computer constraints, but the performance of the segmentation may decrease in this case.

Methods

A new multi-angle projection network (MAPNet) method is proposed for accurately segmenting rib fractures by means of a deep learning approach. The proposed method incorporates multi-angle projection images to complementarily and comprehensively extract the rib characteristics using a rib extraction (RE) module and the fracture features using a fracture segmentation (FS) module. A multi-angle projection fusion (MPF) module is designed for fusing multi-angle spatial features.

Results

It is shown that MAPNet can capture more detailed rib fracture features than some commonly used segmentation networks. Our method achieves a better performance in accuracy (88.06 ± 6.97%), sensitivity (89.26 ± 5.69%), specificity (87.58% ± 7.66%) and in terms of classical criteria like dice (85.41 ± 3.35%), intersection over union (IoU, 80.37 ± 4.63%), and Hausdorff distance (HD, 4.34 ± 3.1).

Conclusion

We propose a rib fracture segmentation technique to deal with the problem of automatic fracture diagnosis. The proposed method avoids the down-sampling of 3D CT data through a projection technique. Experimental results show that it has excellent potential for clinical applications.

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Acknowledgements

This work was supported in part by the State’s Key Project of Research and Development Plan under Grants 2017YFA0104302, 2017YFC0109202, and 2017YFC0107900, in part by National Natural Science Foundation under Grants 81530060 and 61871117, in part by the Science and Technology Program of Guangdong under Grant 2018B030333001, in part by the Key R&D Joint Project of Liaoning under Grant 2020JH-10300164.

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Authors and Affiliations

  1. Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China

    Yuan Gao, Zhan Wu, Hui Tang & Yang Chen

  2. Department of Information, Hainan Hospital of Chinese PLA General Hospital, Sanya, 572013, China

    Han Chen

  3. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Rongjun Ge

  4. Department of Medical Imaging, School of Medicine, Jinling Hospital, Nanjing University, Nanjing, 210002, China

    Dazhi Gao

  5. Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China

    Xiaoli Mai

  6. Department of Radiology, General Hospital of the Northern Theater of the Chinese People’s Liberation Army, Shenyang, 110016, China

    Libo Zhang & Benqiang Yang

  7. Centre de Recherche en Information Biomédicale Sino-Francais, Inserm, University of Rennes 1, 35042, Rennes, France

    Jean-Louis Coatrieux

  8. Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China

    Yuan Gao, Zhan Wu, Hui Tang, Yang Chen & Jean-Louis Coatrieux

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  1. Yuan Gao
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Correspondence to Hui Tang or Dazhi Gao.

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Gao, Y., Chen, H., Ge, R. et al. Deep learning-based framework for segmentation of multiclass rib fractures in CT utilizing a multi-angle projection network. Int J CARS 17, 1115–1124 (2022). https://doi.org/10.1007/s11548-022-02607-1

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