A Method for Semantic Knee Bone and Cartilage Segmentation with Deep 3D Shape Fitting Using Data from the Osteoarthritis Initiative

• Published in: Shape in Medical Imaging Vol. 12474; pp. 85 - 94
• Main Authors: Schock, Justus, Kopaczka, Marcin, Agthe, Benjamin, Huang, Jie, Kruse, Paul, Truhn, Daniel, Conrad, Stefan, Antoch, Gerald, Kuhl, Christiane, Nebelung, Sven, Merhof, Dorit
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2020; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Knee cartilage segmentation; Landmark detection; Shape regression; Statistical shape model
• ISBN: 3030610551; 9783030610555
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-61056-2_7

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.