A Method for Semantic Knee Bone and Cartilage Segmentation with Deep 3D Shape Fitting Using Data from the Osteoarthritis Initiative
Schock J, Kopaczka M, Agthe B, Huang J, Kruse P, Truhn D, Conrad S, Antoch G, Kuhl C, Nebelung S, Merhof D (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12474 LNCS
Pages Range: 85-94
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Lima, PER
ISBN: 9783030610555
DOI: 10.1007/978-3-030-61056-2_7
Abstract
We present a multistage method for deep semantic segmentation of bone structures based on a landmark-based shape regression and subsequent local segmentation of relevant areas. Our solution covers the entire pipeline from 2D-based pre-segmentation, a method for fast deep 3D shape regression and subsequent patch-based 3D semantic segmentation for final segmentation. Since we perform landmark regression using a statistical shape model, our method is able to fit an arbitrary number of landmarks without increase in model complexity. The algorithm is evaluated on the OAI-ZIB dataset, for which we use the binary masks to generate sets of corresponding landmarks and build a deep statistical shape model. By employing our proposed deep shape fitting, our method achieves the performance of existing high-precision approaches in terms of segmentation accuracy while at the same time drastically reducing computational complexity and improving runtime by a large margin.
Involved external institutions
Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen
Germany (DE)
Heinrich-Heine-Universität Düsseldorf
Germany (DE)
Universitätsklinikum Düsseldorf
Germany (DE)
Germany (DE)
Heinrich-Heine-Universität Düsseldorf
Germany (DE)
Universitätsklinikum Düsseldorf
Germany (DE)
How to cite
APA:
Schock, J., Kopaczka, M., Agthe, B., Huang, J., Kruse, P., Truhn, D.,... Merhof, D. (2020). A Method for Semantic Knee Bone and Cartilage Segmentation with Deep 3D Shape Fitting Using Data from the Osteoarthritis Initiative. In Martin Reuter, Martin Reuter, Christian Wachinger, Hervé Lombaert, Hervé Lombaert, Beatriz Paniagua, Orcun Goksel, Islem Rekik (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 85-94). Lima, PER: Springer Science and Business Media Deutschland GmbH.
MLA:
Schock, Justus, et al. "A Method for Semantic Knee Bone and Cartilage Segmentation with Deep 3D Shape Fitting Using Data from the Osteoarthritis Initiative." Proceedings of the International Workshop on Shape in Medical Imaging, ShapeMI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020, Lima, PER Ed. Martin Reuter, Martin Reuter, Christian Wachinger, Hervé Lombaert, Hervé Lombaert, Beatriz Paniagua, Orcun Goksel, Islem Rekik, Springer Science and Business Media Deutschland GmbH, 2020. 85-94.
BibTeX: Download