Skip to main content
SpringerLink
Log in

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

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Pieracci FM, Majercik S, Ali-Osman F, Ang D, Doben A, Edwards JG, French B, Gasparri M, Marasco S, Minshall C, Sarani B, Tisol W, VanBoerum DH, White TW (2016) Consensus statement: surgical stabilization of rib fractures rib fracture colloquium clinical practice guidelines. Injury 48(2):307–321

    Article  Google Scholar 

  2. Cho SH, Sung YM, Kim MS (2012) Missed rib fractures on evaluation of initial chest CT for trauma patients: pattern analysis and diagnostic value of coronal multiplanar reconstruction images with multidetector row CT. Br J Radiol 85(1018):e845–e850

    Article  CAS  Google Scholar 

  3. Lin FC, Li R, Tung Y, Jeng K, Tsai SC (2016) Morbidity, mortality, associated injuries, and management of traumatic rib fractures. J Chin Med Assoc 79(6):329–334

    Article  Google Scholar 

  4. Talbot BS, Gange CP, Chaturvedi A, Klionsky N, Hobbs SK, Chaturvedi A (2017) Traumatic rib injury: patterns, imaging pitfalls, complications, and treatment. Radiographics 37(2):628–651

    Article  Google Scholar 

  5. Ke S, Duan H, Cai Y, Kang J, Feng Z (2014) Thoracoscopy-assisted minimally invasive surgical stabilization of the anterolateral flail chest using Nuss bars. Ann Thorac Surg 97(6):2179–2182

    Article  Google Scholar 

  6. Murphy CE, Raja AS, Baumann BM, Medak AJ, Langdorf MI, Nishijima DK, Hendey GW, Mower WR, Rodriguez RM (2017) Rib fracture diagnosis in the panscan era. Ann Emerg Med 70(6):904–909

    Article  Google Scholar 

  7. Langdorf MI, Medak AJ, Hendey GW, Nishijima DK, Mower WR, Raja AS, Baumann BM, Anglin DR, Anderson CL, Lotfipour S, Reed KE, Zuabi N, Khan NA, Bithell CA, Rowther AA, Villar J, Rodriguez RM (2015) Prevalence and clinical import of thoracic injury identified by chest computed tomography but not chest radiography in blunt trauma: multicenter prospective cohort study. Ann Emerg Med 66(6):589–600

    Article  Google Scholar 

  8. Kim J, Kim S, Kim YJ, Kim KG, Park J (2013) Quantitative measurement method for possible rib fractures in chest radiographs. Healthcare Inform Res 19(3):196–204

    Article  Google Scholar 

  9. Kim EY, Yang HJ, Sung YM, Hwang K, Kim JH, Kim HS (2012) Sternal fracture in the emergency department: diagnostic value of multidetector CT with sagittal and coronal reconstruction images. Eur J Radiol 81(5):e708–e711

    Article  Google Scholar 

  10. Sollmann N, Mei K, Hedderich DM, Maegerlein C, Kopp FK, Löffler MT, Zimmer C, Rummeny EJ, Kirschke JS, Baum T, Noël PB (2019) Multi-detector CT imaging : impact of virtual tube current reduction and sparse sampling on detection of vertebral fractures. Eur Radiol 29(7):3606–3616

    Article  Google Scholar 

  11. Urbaneja A, Verbizier JD, Formery A, Tobon-Gomez C, Nace L, Blum A, Teixeira PAG (2019) Automatic rib cage unfolding with CT cylindrical projection reformat in polytraumatized patients for rib fracture detection and characterization: feasibility and clinical application. Eur J Radiol 110:121–127

    Article  Google Scholar 

  12. Dankerl P, Seuss H, Ellmann S, Cavallaro A, Uber M, Hammon M (2017) Evaluation of rib fractures on a single-in-plane image reformation of the rib cage in CT examinations. Acad Radiol 24(2):153–159

    Article  Google Scholar 

  13. Ringl H, Lazar M, Töpker M, Woitek R, Prosch H, Asenbaum U, Balassy C, Toth D, Weber M, Hajdu S, Soza G, Wimmer A, Mang T (2015) The ribs unfolded-a CT visualization algorithm for fast detection of rib fractures: effect on sensitivity and specificity in trauma patients. Eur Radiol 25(7):1865–1874

    Article  Google Scholar 

  14. Janowczyk A, Madabhushi A (2016) Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J Pathol Inform 7

  15. Alom Z, Yakopcic C, Nasrin MS, Taha TM, Asari VK (2019) Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network. J Digit Imag 32(4):605–617

    Article  Google Scholar 

  16. Zhao Z, Zheng P, Xu S, Wu X (2019) Object detection with deep learning : a review. IEEE Trans Neural Netw Learn Syst 30(11):3212–3232

    Article  Google Scholar 

  17. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widne K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410

    Article  Google Scholar 

  18. Kermany DS, Goldbaum M, Cai W, Lewis MA (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122-1131.e9

    Article  CAS  Google Scholar 

  19. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118

    Article  CAS  Google Scholar 

  20. Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM, Chang YC, Huang CS, Shen D, Chen CM (2016) Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep 6(1):1–13

    Article  Google Scholar 

  21. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y (2017) Artificial intelligence in healthcare: past, present and future. Stroke Vascular Neurol 2(4)

  22. Weikert T, Noordtzij LA, Bremerich J, Stieltjes B, Parmar V, Cyriac J, Sommer G, Sauter AW (2020) Assessment of a deep learning algorithm for the detection of rib fractures on whole-body trauma computed tomography. Korean J Radiol 21(7):891

    Article  Google Scholar 

  23. Zhou QQ, Tang W, Wang J, Hu ZC, Xia ZY, Zhang R, Fan X, Yong W, Yin X, Zhang B, Zhang H (2021) Automatic detection and classification of rib fractures based on patients’ CT images and clinical information via convolutional neural network. Eur Radiol 31(6):3815–3825

    Article  Google Scholar 

  24. Jin L, Yang J, Kuang K, Ni B, Gao Y, Sun Y, Gao P, Ma W, Tan M, Kang H, Chen J, Li M (2020) Deep-learning-assisted detection and segmentation of rib fractures from CT scans: development and validation of FracNet. EBioMedicine 62:103106

    Article  Google Scholar 

  25. Kallel F, Hamida AB (2017) A new adaptive gamma correction based algorithm using DWT-SVD for non-contrast CT image enhancement. IEEE Trans Nanobiosci 16(8):666–675

    Article  Google Scholar 

  26. Tiwari M, Gupta B (2016) Brightness preserving contrast enhancement of medical images using adaptive gamma correction and homomorphic filtering. In: IEEE Students' conference on electrical, electronics and computer science (SCEECS), 1–4

  27. Somasundaram K, Kalavathi P (2011) Medical image contrast enhancement based on gamma correction. Int J Knowl Manag e-Learn 3(1):15–18

    Google Scholar 

  28. Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz CP, Patel BN, Yeom KW, Shpanskaya K, Blankenberg FG, Seekins J, Amrhein TJ, Mong DA, Halabi SS, Zucker EJ, Ng AY, Lungren MP (2018) Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med 15(11):e1002686

    Article  Google Scholar 

  29. Feng S, Zhao H, Shi F, Cheng X, Wang M, Ma Y, Xiang D, Zhu W, Chen X (2020) Cpfnet: context pyramid fusion network for medical image segmentation. IEEE Trans Med Imag 39(10):3008–3018

    Article  Google Scholar 

  30. Zhang W, Yang G, Huang H, Yang W, Xu X, Liu Y, Lai X (2021) ME-Net: Multi-encoder net framework for brain tumor segmentation. Int J Imag Syst Technol 31(4):1834–1848

    Article  Google Scholar 

  31. Li M, Wang C, Zhang H, Yang G (2020) MV-RAN: multiview recurrent aggregation network for echocardiographic sequences segmentation and full cardiac cycle analysis. Comput Biol Med 120:103728

    Article  Google Scholar 

  32. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. International conference on medical image computing and computer-assisted intervention, 424–432

  33. Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, Glocker B, Rueckert D (2018) Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999

  34. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, 234–241

Download references

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.

Author information

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

Authors
  1. Yuan Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Han Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rongjun Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hui Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dazhi Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiaoli Mai
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Libo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Benqiang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Jean-Louis Coatrieux
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Hui Tang or Dazhi Gao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

For this type of study, formal consent is not required.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-022-02607-1

Keywords

Search

Navigation