Abstract
The first step in automated analysis of medical volumetric data is to detect slices, where specific body parts are located. In our project, we aimed to extract the pelvis regionfrom whole-body CT scans. Two deep learning approaches, namely, an unsupervised slice score regressor, and a supervised slice classification method, were evaluated on a relatively small-sized dataset. The result comparison showed that both methods could detect the region of interest with accuracy above 93%. Although the straightforward classification method delivered more accurate results (accuracy of 99%), sometimes it tended to output discontinuous regions, which can be solved by combination of both approaches.
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References
Yan Z, Zhan Y, Peng Z, et al. Multi-instance deep learning: discover discriminative local anatomies for bodypart recognition. IEEE Trans Med Imaging. 2016;35(5):1332–1343.
Khoshelham K. Extending generalized hough transform to detect 3d objects in laser range data. In: ISPRS Workshop on Laser Scanning and SilviLaser 2007, 12-14 September 2007, Espoo, Finland. International Society for Photogrammetry and Remote Sensing; 2007. .
Seim H, Kainmueller D, Heller M, et al. Automatic segmentation of the pelvic bones from CT data based on a statistical shape model. VCBM. 2008;8:93–100.
Roth HR, Lee CT, Shin H, et al. Anatomy-specific classification of medical images using deep convolutional nets. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI); 2015. p. 101–104.
Yan K, Lu L, Summers RM. Unsupervised body part regression via spatially self-ordering convolutional neural networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE; 2018. p. 1022–1025.
Furlow B. Whole-body computed tomography trauma imaging. Radiol Technol. 2017;89(2):159CT–180CT.
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556. 2014;.
Yan L, Lu L, Summers RM;. https://github.com/rsummers11/.
Implementation in Keras by Gabriel Chartrand;. https://github.com/Gabsha/ssbr.
IRCAD Dataset;. https://www.ircad.fr/research/3dircadb/.
DenOtter TD, Schubert J. Houns_eld unit. In: StatPearls [Internet]. StatPearls Publishing; 2019. .
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© 2021 Der/die Autor(en), exklusiv lizenziert durch Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Ivanovska, T., Paulus, A.O., Martin, R., Panahi, B., Schilling, A. (2021). On Efficient Extraction of Pelvis Region from CT Data. In: Palm, C., Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2021. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-33198-6_65
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DOI: https://doi.org/10.1007/978-3-658-33198-6_65
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