Abstract
Imaging plays a crucial role in treatment planning for lumbar spine related problems. Magnetic Resonance Imaging (MRI) in particular holds great potential for visualizing soft tissue and, to a lesser extent, the bones, thus enabling the construction of detailed patient-specific 3D anatomical models. One challenge in MRI of the lumbar spine is that the images are acquired with thick slices to shorten acquisitions in order to minimize patient discomfort as well as motion artifacts. In this work we investigate whether detailed 3D segmentation of the vertebrae can be obtained from thick-slice acquisitions. To this end, we extend a state-of-the-art segmentation algorithm with a simple segmentation reconstruction network, which aims to recover fine-scale shape details from segmentations obtained from thick-slice images. The overall method is evaluated on a paired dataset of MRI and corresponding Computed Tomography (CT) images for a number of subjects. Fine scale segmentations obtained from CT are compared with those reconstructed from thick-slice MRI. Results demonstrate that detailed 3D segmentations can be recovered to a great extent from thick-slice MRI acquisitions for the vertebral bodies and processes in the lumbar spine.
Partially supported by Hochschulmedizin Zürich through the Surgent project.
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Turella, F. et al. (2021). High-Resolution Segmentation of Lumbar Vertebrae from Conventional Thick Slice MRI. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12901. Springer, Cham. https://doi.org/10.1007/978-3-030-87193-2_65
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