# A new Level-set based Protocol for Accurate Bone Segmentation from CT Imaging

arXiv: https://arxiv.org/abs/1505.03093 · HTML: https://mlsanity.com/paper/1505.03093/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2015.

## Built on by (most cited first, 2 of 2)

- [A Survey of Semen Quality Evaluation in Microscopic Videos Using Computer Assisted Sperm Analysis](https://mlsanity.com/paper/2202.07820/) (2022)
- [A Deep Learning-Based Approach to Extracting Periosteal and Endosteal Contours of Proximal Femur in Quantitative CT Images](https://mlsanity.com/paper/2102.01990/) (2021)

## Abstract (arXiv)

In this work it is proposed a medical image segmentation pipeline for accurate bone segmentation from CT imaging. It is a two-step methodology, with a pre-segmentation step and a segmentation refinement step. First, the user performs a rough segmenting of the desired region of interest. Next, a fully automatic refinement step is applied to the pre-segmented data. The automatic segmentation refinement is composed by several sub-stpng, namely image deconvolution, image cropping and interpolation. The user-defined pre-segmentation is then refined over the deconvolved, cropped, and up-sampled version of the image. The algorithm is applied in the segmentation of CT images of a composite femur bone, reconstructed with different reconstruction protocols. Segmentation outcomes are validated against a gold standard model obtained with coordinate measuring machine Nikon Metris LK V20 with a digital line scanner LC60-D that guarantees an accuracy of 28 μ m. High sub-pixel accuracy models were obtained for all tested Datasets. The algorithm is able to produce high quality segmentation of the composite femur regardless of the surface meshing strategy used.