Mr. Teodor Trojner Profile

Teodor Trojner

at Univ. Medical Ctr. Maribor

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Proceedings Article | 2 April 2024 Poster + Paper

Mid-sagittal cross-section identification for vertebra landmarking in MR spine images

Kamil Ibragimov, Gašper Podobnik, Teodor Trojner, Gregor Rečnik, Tomaž Vrtovec

Proceedings Volume 12926, 1292637 (2024) https://doi.org/10.1117/12.3007593

KEYWORDS: Magnetic resonance imaging, Spine, 3D image processing, Image segmentation, X-rays, X-ray imaging, Deformation, Deep learning, Convolution

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Geometrical parameters of the spine have been identified as valuable for describing spinal deformities, and their measurement is commonly based on landmarks, identified in radiographs but often also in three-dimensional (3D) magnetic resonance (MR) images of the spine. However, existing studies make use of manually pre-selected midsagittal cross-sections of 3D MR images that best align with the middle of the spines. In this work, we propose an algorithm for lumbar vertebra landmarking in 3D MR images by using a combination of neural networks for the identification of the correct mid-sagittal cross-section and landmark area segmentation. In a database of 3D MR images from 70 patients, four landmark groups were defined on lumbar vertebrae and sacral endplates: antero-superior, postero-superior, anterior-inferior and postero-interior. A ResNet was first applied to estimate the displacements of sagittal cross-sections and obtain the location of the mid-sagittal cross-section within the 3D image. A U-Net was then used to segment the landmark areas in several cross-sections that were predicted as candidates for the mid-sagittal cross-section. Both networks were finally combined into a landmark detection framework. The overall landmark detection error, measured as the mean absolute distance (± standard deviation) between manually placed and automatically detected landmarks, was 2.2±1.3 mm, with 92.7% of all landmarks detected correctly. The resulting error can be decomposed into 1.6±1.4mm along the sagittal direction (ResNet) and 1.3±0.9mm within the sagittal plane (U-Net), which is close to human performance.

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