Bibliographic Details
| Title: |
Automatic 3D Postoperative Evaluation of Complex Orthopaedic Interventions |
| Authors: |
Joëlle Ackermann, Armando Hoch, Jess Gerrit Snedeker, Patrick Oliver Zingg, Hooman Esfandiari, Philipp Fürnstahl |
| Source: |
Journal of Imaging, Vol 9, Iss 9, p 180 (2023) |
| Publisher Information: |
MDPI AG, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Computer applications to medicine. Medical informatics
LCC:Electronic computers. Computer science |
| Subject Terms: |
orthopaedic computer science, cut detection, postoperative evaluation, machine learning, deep learning, segmentation, Photography, TR1-1050, Computer applications to medicine. Medical informatics, R858-859.7, Electronic computers. Computer science, QA75.5-76.95 |
| More Details: |
In clinical practice, image-based postoperative evaluation is still performed without state-of-the-art computer methods, as these are not sufficiently automated. In this study we propose a fully automatic 3D postoperative outcome quantification method for the relevant steps of orthopaedic interventions on the example of Periacetabular Osteotomy of Ganz (PAO). A typical orthopaedic intervention involves cutting bone, anatomy manipulation and repositioning as well as implant placement. Our method includes a segmentation based deep learning approach for detection and quantification of the cuts. Furthermore, anatomy repositioning was quantified through a multi-step registration method, which entailed a coarse alignment of the pre- and postoperative CT images followed by a fine fragment alignment of the repositioned anatomy. Implant (i.e., screw) position was identified by 3D Hough transform for line detection combined with fast voxel traversal based on ray tracing. The feasibility of our approach was investigated on 27 interventions and compared against manually performed 3D outcome evaluations. The results show that our method can accurately assess the quality and accuracy of the surgery. Our evaluation of the fragment repositioning showed a cumulative error for the coarse and fine alignment of 2.1 mm. Our evaluation of screw placement accuracy resulted in a distance error of 1.32 mm for screw head location and an angular deviation of 1.1° for screw axis. As a next step we will explore generalisation capabilities by applying the method to different interventions. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2313-433X |
| Relation: |
https://www.mdpi.com/2313-433X/9/9/180; https://doaj.org/toc/2313-433X |
| DOI: |
10.3390/jimaging9090180 |
| Access URL: |
https://doaj.org/article/6ef17f79a21e4b0f811d54774f29bb6f |
| Accession Number: |
edsdoj.6ef17f79a21e4b0f811d54774f29bb6f |
| Database: |
Directory of Open Access Journals |
