Bayesian Segmentation of Hip and Thigh Muscles in Metal Artifact-Contaminated CT Using Convolutional Neural Network-Enhanced Normalized Metal Artifact Reduction
| Autor: | Yuki Suzuki, Yoshito Otake, Yuta Hiasa, Nobuhiko Sugano, Yoshinobu Sato, Mitsuki Sakamoto, Masaki Takao |
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| Rok vydání: | 2020 |
| Předmět: |
biology
Computer science medicine.medical_treatment 020206 networking & telecommunications 02 engineering and technology Correlation ratio biology.organism_classification Real image Convolutional neural network Theoretical Computer Science Metal Artifact Medius Hardware and Architecture Control and Systems Engineering Modeling and Simulation Signal Processing Metric (mathematics) 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Segmentation Reduction (orthopedic surgery) Information Systems Biomedical engineering |
| Zdroj: | Journal of Signal Processing Systems. 92:335-344 |
| ISSN: | 1939-8115 1939-8018 |
| DOI: | 10.1007/s11265-019-01507-z |
| Popis: | In total hip arthroplasty, analysis of postoperative medical images is important to evaluate surgical outcome. Since Computed Tomography (CT) is most prevalent modality in orthopedic surgery, we aimed at the analysis of CT image. In this work, we focus on the metal artifact in postoperative CT caused by the metallic implant, which reduces the accuracy of segmentation especially in the vicinity of the implant. Our goal was to develop an automated segmentation method of the bones and muscles in the postoperative CT images. We propose a method that combines Normalized Metal Artifact Reduction (NMAR), which is one of the state-of-the-art metal artifact reduction methods, and a Convolutional Neural Network-based segmentation using two U-net architectures. The first U-net refines the result of NMAR and the Bayesian muscle segmentation is performed by the second U-net. We conducted experiments using simulated images of 20 patients and real images of three patients to evaluate the segmentation accuracy of 19 muscles. In simulation study, the proposed method showed statistically significant improvement (p |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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