# A direct geometry processing cartilage generation method using segmented bone models from datasets with poor cartilage visibility

arXiv: https://arxiv.org/abs/2203.10667 · HTML: https://mlsanity.com/paper/2203.10667/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 36 references that are in the map.
- First posted on arXiv 20 Mar 2022.

## Built on by (most cited first, 1 of 1)

- [Auto-segmentation of Hip Joints using MultiPlanar UNet with Transfer learning](https://mlsanity.com/paper/2208.08226/) (2022)

## Abstract (arXiv)

We present a method to generate subject-specific cartilage for the hip joint. Given bone geometry, our approach is agnostic to image modality, creates conforming interfaces, and is well suited for finite element analysis. We demonstrate our method on ten hip joints showing anatomical shape consistency and well-behaved stress patterns. Our method is fast and may assist in large-scale biomechanical population studies of the hip joint when manual segmentation or training data is not feasible.