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Segmentation and classification of total hip endoprosthesis in x-ray images

  • Motivation: Aim of this project is the automatic classification of total hip endoprosthesis (THEP) components in 2D Xray images. Revision surgeries of total hip arthroplasty (THA) are common procedures in orthopedics and trauma surgery. Currently, around 400.000 procedures per year are performed in the United States (US) alone. To achieve the best possible result, preoperative planning is crucial. Especially if parts of the current THEP system are to be retained. Methods: First, a ground truth based on 76 X-ray images was created: We used an image processing pipeline consisting of a segmentation step performed by a convolutional neural network and a classification step performed by a support vector machine (SVM). In total, 11 classes (5 pans and 6 shafts) shall be classified. Results: The ground truth generated was of good quality even though the initial segmentation was performed by technicians. The best segmentation results were achieved using a U-net architecture. For classification, SVM architectures performed much better than additional neural networks. Conclusions: The overall image processing pipeline performed well, but the ground truth needs to be extended to include a broader variability of implant types and more examples per training class.

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Metadaten
Author of HS ReutlingenBurgert, Oliver; Büchner, Hannah; Malik, Maximilian
DOI:https://doi.org/10.1117/12.2610770
Erschienen in:Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, Proc. of SPIE Vol. 12034, 20 February - 28 March 2022, San Diego, United States
Publisher:SPIE
Place of publication:Bellingham, Wash.
Document Type:Conference Proceeding
Language:English
Year of Publication:2022
Tag:artificial intelligence; image classification; image processing; image segmentation; medical imaging; surgery; x-ray imaging; x-rays
Page Number:5
First Page:120341T-1
Last Page:120341T-5
DDC classes:610 Medizin, Gesundheit
Open Access?:Nein