Abstract
Manually annotating medical images with few landmarks to initialize 3D shape models is a common practice. For instance, when reconstructing the 3D spine from biplanar X-rays, the spinal midline, passing through vertebrae body centers (VBCs) and endplate midpoints, is required. This paper presents an automated spinal midline delineation method on frontal and sagittal views by using Mask R-CNN. The network detects all vertebrae from C7 to L5, followed by vertebrae segmentation and classification at the same time. After postprocessing to discard outliers, the vertebrae mask centers were regarded as VBCs to get the spine midline by polynomial fitting. Evaluation of the spinal midline on 136 images used root mean square error (RMSE) with respect to manual ground-truth. The RMSE ± standard error values of predicted spinal midlines (C7–L5) were 1.11 mm ± 0.67 mm on frontal views and 1.92 mm ± 1.38 mm on sagittal views. The proposed method is capable of delineating spinal midlines on patients with different spine deformity degrees.
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References
Skalli, W., Vergari, C., Ebermeyer, E., Courtois, I., Drevelle, X., Kohler, R., Abelin-Genevois, K., Dubousset, J.: Early detection of progressive adolescent idiopathic scoliosis: a severity index. Spine 42(11), 823–830 (2017)
Vergari, C., Courtois, I., Ebermeyer, E., Bouloussa, H., Vialle, R., Skalli, W.: Experimental validation of a patient-specific model of orthotic action in adolescent idiopathic scoliosis. Eur. Spine J. 25(10), 3049–3055 (2016)
Brenner, D.J., Hall, E.J.: Computed tomography—an increasing source of radiation exposure. N. Engl. J. Med. 357(22), 2277–2284 (2007)
Yazici, M., Acaroglu, E.R., Alanay, A., Deviren, V., Cila, A., Surat, A.: Measurement of vertebral rotation in standing versus supine position in adolescent idiopathic scoliosis. J. Pediatr. Orthop. 21(2), 252–256 (2001)
Dubousset, J., Charpak, G., Dorion, I., Skalli, W., Lavaste, F., Deguise, J., Kalifa, G., Ferey, S.: A new 2D and 3D imaging approach to musculoskeletal physiology and pathology with low-dose radiation and the standing position: the EOS system. Bull. de l’Academie nationale de medecine 189(2), 287–297 (2005)
Humbert, L., De Guise, J.A., Aubert, B., Godbout, B., Skalli, W.: 3D reconstruction of the spine from biplanar X-rays using parametric models based on transversal and longitudinal inferences. Med. Eng. Phys. 31(6), 681–687 (2009)
Ilharreborde, B., Steffen, J.S., Nectoux, E., Vital, J.M., Mazda, K., Skalli, W., Obeid, I.: Angle measurement reproducibility using EOS three-dimensional reconstructions in adolescent idiopathic scoliosis treated by posterior instrumentation. Spine 36(20), E1306–E1313 (2011)
Gajny, L., Ebrahimi, S., Vergari, C., Angelini, E., Skalli, W.: Quasi-automatic 3D reconstruction of the full spine from low dose biplanar X-rays based on statistical inferences and image analysis. Eur. Spine J. 28(4), 658–664 (2019)
Galbusera, F., Niemeyer, F., Wilke, H.J., Bassani, T., Casaroli, G., Anania, C., Costa, F., Brayda-Bruno, M., Sconfienza, L.M.: Fully automated radiological analysis of spinal disorders and deformities: a deep learning approach. Eur. Spine J. 28, 1–10 (2019)
Aubert, B., Vazquez, C., Cresson, T., Parent, S., De, J.G.: Towards automated 3D spine reconstruction from biplanar radiographs using CNN for statistical spine model fitting. IEEE Trans. Med. Imaging (2019)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
Abdulla, W.: Mask R-CNN for object detection and instance segmentation on keras and tensorflow (2017). https://github.com/matterport/Mask_RCNN
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
Rousseau, M.A., Laporte, S., Chavary-Bernier, E., Lazennec, J.Y., Skalli, W.: Reproducibility of measuring the shape and three-dimensional position of cervical vertebrae in upright position using the EOS stereoradiography system. Spine 32(23), 2569–2572 (2007)
Wu, H., Bailey, C., Rasoulinejad, P., Li, S.: Automated comprehensive Adolescent Idiopathic Scoliosis assessment using MVC-Net. Med. Image Anal. 48, 1–11 (2018)
Horng, M.-H., Kuok, C.-P., Fu, M.-J., Lin, C.-J., Sun, Y.-N.: Cobb angle measurement of spine from X-ray images using convolutional neural network. Comput. Math. Methods Med. 2019, article ID 6357171, 18 p. (2019). https://doi.org/10.1155/2019/6357171
Ebrahimi, S., Gajny, L., Skalli, W., Angelini, E.: Vertebral corners detection on sagittal X-rays based on shape modelling, random forest classifiers and dedicated visual features. Comput. Methods Biomech. Biomed. Eng.: Imaging Vis. 7(2), 132–144 (2019)
Jung, A.: imgaug (2017). https://github.com/aleju/imgaug
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Acknowledgments
The authors thank the ParisTech BiomecAM chair program, on subject-specific musculoskeletal modelling and in particular Société Générale and COVEA. The authors would also like to thank François Girinon for having initiated this work.
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Yang, Z., Skalli, W., Vergari, C., Angelini, E.D., Gajny, L. (2019). Automated Spinal Midline Delineation on Biplanar X-Rays Using Mask R-CNN. In: Tavares, J., Natal Jorge, R. (eds) VipIMAGE 2019. VipIMAGE 2019. Lecture Notes in Computational Vision and Biomechanics, vol 34. Springer, Cham. https://doi.org/10.1007/978-3-030-32040-9_32
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DOI: https://doi.org/10.1007/978-3-030-32040-9_32
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