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A novel bone suppression method that improves lung nodule detection

Suppressing dedicated bone shadows in radiographs while preserving the remaining signal

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

Abstract

Purpose

Suppressing thoracic bone shadows in chest radiographs has been previously reported to improve the detection rates for solid lung nodules, however at the cost of increased false detection rates. These bone suppression methods are based on an artificial neural network that was trained using dual-energy subtraction images in order to mimic their appearance.

Method

Here, a novel approach is followed where all bone shadows crossing the lung field are suppressed sequentially leaving the intercostal space unaffected. Given a contour delineating a bone, its image region is spatially transferred to separate normal image gradient components from tangential component. Smoothing the normal partial gradient along the contour results in a reconstruction of the image representing the bone shadow only, because all other overlaid signals tend to cancel out each other in this representation.

Results

The method works even with highly contrasted overlaid objects such as a pacemaker. The approach was validated in a reader study with two experienced chest radiologists, and these images helped improving both the sensitivity and the specificity of the readers for the detection and localization of solid lung nodules. The AUC improved significantly from 0.596 to 0.655 on a basis of 146 images from patients and normals with a total of 123 confirmed lung nodules.

Conclusion

Subtracting all reconstructed bone shadows from the original image results in a soft image where lung nodules are no longer obscured by bone shadows. Both the sensitivity and the specificity of experienced radiologists increased.

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Notes

  1. Images publicly available from http://www.jsrt.or.jp/jsrt-db/eng.php.

  2. Images publicly available from http://cancerimagingarchive.net.

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Acknowledgments

We would like to thank Martin Haker and Moritz Schaar, both former employees of Philips Research Hamburg, for their algorithmic contribution. We also would like to thank Raoul Florent and Claire Levrier, Philips Medisys, Paris and Harald Heese, Philips Research Hamburg, for some valuable suggestions. We are grateful to Cornelia Schaefer-Prokop, Meander Medical Center Amersfoort, the Netherlands, and Martin Uffmann, Landesklinikum Neunkirchen, Austria, for their assessments in the early phases of the project. We would like to thank C. A. Nuñez at Hospital Sant Pau, Barcelona, for her support in preparing the reader study.

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Authors and Affiliations

  1. Digital Imaging, Philips Research, Hamburg, Germany

    Jens von Berg, Stewart Young, Heike Carolus & Axel Saalbach

  2. Clinical Science, Diagnostix X-Ray, Philips Healthcare, Hamburg, Germany

    Robin Wolz

  3. Department of Radiology, Hospital de la Santa Creu i Sant Pau, Carrer de Sant Quintí, 89, Barcelona, Spain

    Alberto Hidalgo, Ana Giménez & Tomás Franquet

Authors
  1. Jens von Berg
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Corresponding author

Correspondence to Jens von Berg.

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Conflict of interest

J. von Berg, S. Young, H. Carolus, A. Saalbach, and R. Wolz are employees of Philips. A. Hidalgo, A. Giménez, and T. Franquet do not have a conflict of interest.

Appendix

Appendix

In order to prove that

$$\begin{aligned} {I}^l_{r_C}\left( \mathbf {p_i}\right) \mathop {=}\limits ^{?} {I}^c_{r_C}\left( \mathbf {p_c}\right) \end{aligned}$$

we set

$$\begin{aligned}&I_{r_C}\left( \mathbf {p_i}\right) + \frac{I_{r_C}\left( \mathbf {p_c}\right) -I_{r_C}\left( \mathbf {p_i}\right) }{2} \\&\quad \mathop {=}\limits ^{?} I_{r_C}\left( \mathbf {p_c}\right) + \frac{I_{r_C}\left( \mathbf {p_i}\right) -I_{r_C}\left( \mathbf {p_c}\right) }{2} \\&I_{r_C}\left( \mathbf {p_i}\right) -I_{r_C}\left( \mathbf {p_c}\right) \\&\quad \mathop {=}\limits ^{?} \frac{I_{r_C}\left( \mathbf {p_i}\right) -I_{r_C}\left( \mathbf {p_c}\right) - \left( I_{r_C}\left( \mathbf {p_c}\right) -I_{r_C}\left( \mathbf {p_i}\right) \right) }{2} \\&\quad I_{r_C}\left( \mathbf {p_i}\right) -I_{r_C}\left( \mathbf {p_c}\right) = I_{r_C}\left( \mathbf {p_i}\right) -I_{r_C}\left( \mathbf {p_c}\right) \end{aligned}$$

which proves the convergence of \({I}^i_{r_C}\) and \({I}^c_{r_C}\) at the centerline.

Informed consent was obtained from all patients for being included in the study.

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von Berg, J., Young, S., Carolus, H. et al. A novel bone suppression method that improves lung nodule detection. Int J CARS 11, 641–655 (2016). https://doi.org/10.1007/s11548-015-1278-y

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