Vertebral segmentation without training using differentiable appearance modeling of a deformable spine template

Hyunsoo Kim; Jinah Park

doi:10.1117/12.3006602

2 April 2024 Vertebral segmentation without training using differentiable appearance modeling of a deformable spine template

Hyunsoo Kim, Jinah Park

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129262N (2024) https://doi.org/10.1117/12.3006602
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

We present a robust vertebral segmentation framework that can bootstrap the segmentation task with little to no dataset. Our deformable model-based framework jointly optimizes the appearance and shape of the spine model using the novel differentiable appearance modeling method. Because our framework learns the appearance of the spine only from the given image, it does not rely on the dataset or handcrafted image features and adapts robustly to the appearance of the image. With our proposed differentiable signed distance operator and spectral mesh optimization, the shape of the spine model can be refined via a gradient-based optimizer. Our framework was tested on the VerSe’20 training dataset, and it achieved an average Dice score of up to 90% for selected vertebral labels. Our results suggest that utilizing the explicit knowledge from the template model can significantly reduce the need for a large training dataset.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hyunsoo Kim and Jinah Park "Vertebral segmentation without training using differentiable appearance modeling of a deformable spine template", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129262N (2 April 2024); https://doi.org/10.1117/12.3006602

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