2D vs. 3D U-Net Abdominal Organ Segmentation in CT Data Using Organ Bounds

SPIE Medical Imaging Conference 2021

Abstract

We compare axial 2D U-Nets and their 3D counterparts for pixel/voxel-based segmentation of five abdominal organs in CT scans. For each organ, two competing CNNs are trained. They are evaluated by performing five-fold cross-validation on 80 3D images. In a two-step concept, the relevant area containing the organ is first extracted by detected bounding boxes and then passed as input to the organ-specific U-Net. Furthermore, a random regression forest approach for the automatic detection of bounding boxes is summarized from our previous work. The results show that the 2D U-Net is mostly on par with the 3D U-Net or even outperforms it. Especially for the kidneys, it is significantly better suited in this study.

Bibtex

@inproceedings{kern2021segmentation,
	title={2D vs. 3D U-Net Abdominal Organ Segmentation in CT Data Using Organ Bounds},
	author={Kern, Daria and Klauck, Ulrich and Ropinski, Timo and Mastmeyer, Andr{\'e}},
	booktitle={Proceedings of SPIE Medical Imaging 2021 (Volume 11601)}
	year={2021},
	pages={online}
}