Publikationen

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

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

Daria Kern Hochschule Aalen Ulrich Klauck Hochschule Aalen Timo Ropinski Ulm University André Mastmeyer Hochschule Aalen

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

content_copy
@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},
	month={1},
	pages={online}
}