2019-05-24-abdi19a.md

section	title	abstract	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
Contributed Papers	AnatomyGen: Deep Anatomy Generation From Dense Representation With Applications in Mandible Synthesis	This work is an effort in human anatomy synthesis using deep models. Here, we introduce a deterministic deep convolutional architecture to generate human anatomies represented as 3D binarized occupancy maps (voxel-grids). The shape generation process is constrained by the 3D coordinates of a small set of landmarks selected on the surface of the anatomy. The proposed learning framework is empirically tested on the mandible bone where it was able to reconstruct the anatomies from landmark coordinates with the average landmark-to-surface error of 1.42 mm. Moreover, the model was able to linearly interpolate in the $\mathbb{Z}$-space and smoothly morph a given 3D anatomy to another. The proposed approach can potentially be used in semi-automated segmentation with manual landmark selection as well as biomechanical modeling. Our main contribution is to demonstrate that deep convolutional architectures can generate high fidelity complex human anatomies from abstract representations.	inproceedings	Proceedings of Machine Learning Research	abdi19a	0	AnatomyGen: Deep Anatomy Generation From Dense Representation With Applications in Mandible Synthesis	4	14	4-14	4	false	Abdi, {Amir H.} and Borgard, Heather and Abolmaesumi, Purang and Fels, Sidney	given family Amir H. Abdi given family Heather Borgard given family Purang Abolmaesumi given family Sidney Fels	2019-05-24		PMLR	Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning	102	inproceedings	date-parts 2019 5 24	http://proceedings.mlr.press/v102/abdi19a/abdi19a.pdf