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Fast 3D YOLOv3 based standard plane regression of vertebral bodies in intra-operative CBCT volumes
Dörrich, Sebastian; Kordon, Florian; Denzinger, Felix; u. a. (2023): Fast 3D YOLOv3 based standard plane regression of vertebral bodies in intra-operative CBCT volumes, in: Bamberg: Otto-Friedrich-Universität, S. 1–15.
Faculty/Chair:
Publisher Information:
Year of publication:
2023
Pages:
Source/Other editions:
Journal of medical imaging : JMI, 10 (2023), 3, S. 1-15 . - ISSN: 2329-4310
Year of first publication:
2023
Language:
English
Abstract:
Purpose
Mobile C-arm systems represent the standard imaging devices within the field of spine surgery. In addition to 2D imaging, they allow for 3D scans while preserving unrestricted patient access. For viewing, the acquired volumes are adjusted such that their anatomical standard planes align with the axes of the viewing modality. This difficult and time-consuming step is currently performed manually by the leading surgeon. This process is automatized within this work to improve the usability of C-arm systems. Thereby, the spinal region consisting of multiple vertebrae and the standard planes of all vertebrae being of interest to the surgeon need to be taken into account.
Approach
An object detection algorithm based on the you only look once version 3 architecture, adapted to 3D inputs, is compared with a segmentation-based approach employing a 3D U-Net. Both algorithms are trained on a dataset of 440 and tested on 218 spinal volumes.
Results
Although the detection-based algorithm is slightly inferior concerning the detection (91% versus 97% accuracy), localization (1.26 mm versus 0.74 mm error) and alignment accuracy (5.00 deg versus 4.73 deg error), it outperforms the segmentation-based one in terms of speed (5 s versus 38 s).
Conclusions
Both algorithms show similar good results. However, the speed gain of the detection-based algorithm, resulting in a run time of 5 s, makes it more suitable for usage in an intra-operative scenario.
Mobile C-arm systems represent the standard imaging devices within the field of spine surgery. In addition to 2D imaging, they allow for 3D scans while preserving unrestricted patient access. For viewing, the acquired volumes are adjusted such that their anatomical standard planes align with the axes of the viewing modality. This difficult and time-consuming step is currently performed manually by the leading surgeon. This process is automatized within this work to improve the usability of C-arm systems. Thereby, the spinal region consisting of multiple vertebrae and the standard planes of all vertebrae being of interest to the surgeon need to be taken into account.
Approach
An object detection algorithm based on the you only look once version 3 architecture, adapted to 3D inputs, is compared with a segmentation-based approach employing a 3D U-Net. Both algorithms are trained on a dataset of 440 and tested on 218 spinal volumes.
Results
Although the detection-based algorithm is slightly inferior concerning the detection (91% versus 97% accuracy), localization (1.26 mm versus 0.74 mm error) and alignment accuracy (5.00 deg versus 4.73 deg error), it outperforms the segmentation-based one in terms of speed (5 s versus 38 s).
Conclusions
Both algorithms show similar good results. However, the speed gain of the detection-based algorithm, resulting in a run time of 5 s, makes it more suitable for usage in an intra-operative scenario.
GND Keywords: ; ; ; ; ; ; ; ; ; ; ;
Bilderzeugung
Norm <Normung>
3D-Technologie
Objekterkennung
Bildsegmentierung
VOXEL
Computertomograf
Operation
Anatomie
Wirbelsäule
Algorithmus
Mustererkennung
Keywords: ; ; ; ; ; ; ; ; ;
3D imaging standards
Object detection
Image segmentation
Cone beam computed tomography
Surgery
Detection and tracking algorithms
Education and training
Anatomy
Spine
Voxels
DDC Classification:
RVK Classification:
Type:
Article
Activation date:
June 23, 2023
Permalink
https://fis.uni-bamberg.de/handle/uniba/59864