Yoshiyuki Kagiyama1, Itaru Otomaru2,3,4, Masaki Takao5, Nobuhiko Sugano6, Masahiko Nakamoto3,7, Futoshi Yokota8, Noriyuki Tomiyama3, Yukio Tada9, Yoshinobu Sato10. 1. Graduate Faculty of Interdisciplinary Research, University of Yamanashi, 4-3-11, Takeda, Kofu, Yamanashi, Japan. 2. Graduate School of Engineering, Kobe University, 1-1, Rokkodai, Nada, Kobe, Japan. 3. Department of Radiology, Graduate School of Medicine, Osaka University, 2-2, Yamadaoka, Suita, Osaka, Japan. 4. Canon Inc., 3-30-2, Shimomaruko, Ota, Tokyo, Japan. 5. Department of Orthopaedic Surgery, Graduate School of Medicine, Osaka University, 2-2, Yamadaoka, Suita, Osaka, Japan. 6. Department of Orthopaedic Medical Engineering, Graduate School of Medicine, Osaka University, 2-2, Yamadaoka, Suita, Osaka, Japan. 7. EBM Inc., 4-6-15, Omoriminami, Ota, Tokyo, Japan. 8. Graduate School of Information Science, Nara Institute of Science and Technology (NAIST), 8916-5, Takayama, Ikoma, Nara, Japan. 9. Graduate School of System Informatics, Kobe University, 1-1, Rokkodai, Nada, Kobe, Japan. 10. Graduate School of Information Science, Nara Institute of Science and Technology (NAIST), 8916-5, Takayama, Ikoma, Nara, Japan. yoshi@is.naist.jp.
Abstract
PURPOSE: This study describes the use of CT images in atlas-based automated planning methods for acetabular cup implants in total hip arthroplasty (THA). The objective of this study is to develop an automated cup planning method considering the statistical distribution of the residual thickness. METHODS: From a number of past THA planning datasets, we construct two statistical atlases that represent the surgeon's expertise. The first atlas is a pelvis-cup merged statistical shape model (PC-SSM), which encodes global spatial relationships between the patient anatomy and implant. The other is a statistical residual thickness map (SRTM) of the implant surface, which encodes local spatial constraints of the anatomy and implant. In addition to PC-SSM and SRTM, we utilized the minimum thickness as a threshold constraint to prevent penetration. RESULTS: The proposed method was applied to the pelvis shapes segmented from CT images of 37 datasets of osteoarthritis patients. Automated planning results with manual segmentation were compared to the plans prepared by an experienced surgeon. There was no significant difference in the average cup size error between the two methods (1.1 and 1.2 mm, respectively). The average positional error obtained by the proposed method, which integrates the two atlases, was significantly smaller (3.2 mm) than the previous method, which uses single atlas (3.9 mm). In the proposed method with automated segmentation, the size error of the proposed method for automated segmentation was comparable (1.1 mm) to that for manual segmentation (1.1 mm). The average positional error was significantly worse (4.2 mm) than that using manual segmentation (3.2 mm). If we only consider mildly diseased cases, however, there was no significance between them (3.2 mm in automated and 2.6 mm in manual segmentation). CONCLUSION: We infer that integrating PC-SSM and SRTM is a useful approach for modeling experienced surgeon's preference during cup planning.
PURPOSE: This study describes the use of CT images in atlas-based automated planning methods for acetabular cup implants in total hip arthroplasty (THA). The objective of this study is to develop an automated cup planning method considering the statistical distribution of the residual thickness. METHODS: From a number of past THA planning datasets, we construct two statistical atlases that represent the surgeon's expertise. The first atlas is a pelvis-cup merged statistical shape model (PC-SSM), which encodes global spatial relationships between the patient anatomy and implant. The other is a statistical residual thickness map (SRTM) of the implant surface, which encodes local spatial constraints of the anatomy and implant. In addition to PC-SSM and SRTM, we utilized the minimum thickness as a threshold constraint to prevent penetration. RESULTS: The proposed method was applied to the pelvis shapes segmented from CT images of 37 datasets of osteoarthritispatients. Automated planning results with manual segmentation were compared to the plans prepared by an experienced surgeon. There was no significant difference in the average cup size error between the two methods (1.1 and 1.2 mm, respectively). The average positional error obtained by the proposed method, which integrates the two atlases, was significantly smaller (3.2 mm) than the previous method, which uses single atlas (3.9 mm). In the proposed method with automated segmentation, the size error of the proposed method for automated segmentation was comparable (1.1 mm) to that for manual segmentation (1.1 mm). The average positional error was significantly worse (4.2 mm) than that using manual segmentation (3.2 mm). If we only consider mildly diseased cases, however, there was no significance between them (3.2 mm in automated and 2.6 mm in manual segmentation). CONCLUSION: We infer that integrating PC-SSM and SRTM is a useful approach for modeling experienced surgeon's preference during cup planning.
Authors: A M DiGioia; B Jaramaz; M Blackwell; D A Simon; F Morgan; J E Moody; C Nikou; B D Colgan; C A Aston; R S Labarca; E Kischell; T Kanade Journal: Clin Orthop Relat Res Date: 1998-10 Impact factor: 4.176
Authors: D Dammerer; A Keiler; S Herrnegger; D Putzer; S Strasser; M Liebensteiner Journal: Arch Orthop Trauma Surg Date: 2021-03-16 Impact factor: 2.928