2D vs. 3D U-Net abdominal organ segmentation in CT data using organ bounds

Daria Kern; Ulrich Klauck; Timo Ropinski; André Mastmeyer

doi:10.1117/12.2576168

15 February 2021 2D vs. 3D U-Net abdominal organ segmentation in CT data using organ bounds

Daria Kern, Ulrich Klauck, Timo Ropinski, André Mastmeyer

Proceedings Volume 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications; 1160114 (2021) https://doi.org/10.1117/12.2576168
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

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.

Conference Presentation

Citation Download Citation

Daria Kern, Ulrich Klauck, Timo Ropinski, and André Mastmeyer "2D vs. 3D U-Net abdominal organ segmentation in CT data using organ bounds", Proc. SPIE 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications, 1160114 (15 February 2021); https://doi.org/10.1117/12.2576168

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available