Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis [article]

Anjany Sekuboyina, Markus Rempfler, Alexander Valentinitsch, Maximilian Loeffler, Jan S. Kirschke, Bjoern H. Menze
2019 arXiv   pre-print
We propose an auto-encoding network architecture for point clouds (PC) capable of extracting shape signatures without supervision. Building on this, we (i) design a loss function capable of modelling data variance on PCs which are unstructured, and (ii) regularise the latent space as in a variational auto-encoder, both of which increase the auto-encoders' descriptive capacity while making them probabilistic. Evaluating the reconstruction quality of our architectures, we employ them for
more » ... vertebral fractures without any supervision. By learning to efficiently reconstruct only healthy vertebrae, fractures are detected as anomalous reconstructions. Evaluating on a dataset containing ∼1500 vertebrae, we achieve area-under-ROC curve of >75 without using intensity-based features.
arXiv:1907.09254v2 fatcat:pqbjp44zi5bnxciz4luowu5oem