Zusammenfassung
In this paper, we present a 2D deep multitask learning approach for the segmentation of small structures on the example of avascular necrosis of the femoral head (AVNFH) in MRI. It consists of one joint encoder and three separate decoder branches, each assigned to its own objective. We propose using a reconstruction task to initially pre-train the encoder and shift the objective towards a second necrosis segmentation task in a reconstruction-dependent loss adaptation manner. The third branch deals with the rough localization of the topographical neighborhood of possible femoral necrosis areas. Its output is used to emphasize the roughly approximated location of the segmentation branch’s output. The evaluation of the segmentation performance of our architecture on coronal T1-weighted MRI volumes shows promising improvements compared to a standard U-Net implementation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Literatur
Baig SA, Baig M. Osteonecrosis of the femoral head: etiology, investigations, and management. Cureus. 2018;10(8).
Zoroofi RA, Nishii T, Sato Y, et al. Segmentation of avascular necrosis of the femoral head using 3-d MR images. Comput Med Imaging Graph. 2001;25(6):511–521.
Song B, Chou CR, Chen X, et al. Anatomy-guided brain tumor segmentation and classification. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer International Publishing; 2016. p. 162–170.
Christ PF, Elshaer MEA, Ettlinger F, et al. Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3d conditional random fields. In: Proc MICCAI. Springer; 2016. p. 415–423.
Vorontsov E, Tang A, Pal C, et al. Liver lesion segmentation informed by joint liver segmentation. In: Proc IEEE ISBI; 2018. p. 1332–1335.
Hatamizadeh A, Hoogi A, Sengupta D, et al. Deep active lesion segmentation. In: Suk HI, Liu M, Yan P, et al., editors. Machine Learning in Medical Imaging. Cham: Springer International Publishing; 2019. p. 98–105.
Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2015. p. 234–241.
Erhan D, Bengio Y, Courville A, et al. Why does unsupervised pre-training help deep learning? J Mach Learn Res. 2010;11(Feb):625–660.
Wiehman S, Kroon S, De Villiers H. Unsupervised pre-training for fully convolutional neural networks. In: 2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech). IEEE; 2016. p. 1–6.
Pham DD, Dovletov G, Warwas S, et al. Deep segmentation refinement with result-dependent learning. In: Bildverarbeitung für die Medizin 2019. Wiesbaden: Springer Fachmedien Wiesbaden; 2019. p. 49–54.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Pham, D.D., Dovletov, G., Serong, S., Landgraeber, S., Jäger, M., Pauli, J. (2020). Multitask-Learning for the Extraction of Avascular Necrosis of the Femoral Head in MRI. In: Tolxdorff, T., Deserno, T., Handels, H., Maier, A., Maier-Hein, K., Palm, C. (eds) Bildverarbeitung für die Medizin 2020. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-29267-6_31
Download citation
DOI: https://doi.org/10.1007/978-3-658-29267-6_31
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-29266-9
Online ISBN: 978-3-658-29267-6
eBook Packages: Computer Science and Engineering (German Language)