We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI.
- Authors
Astono, Indriani P.; Welsh, James S.; Chalup, Stephan; Greer, Peter
- Abstract
In this paper, we develop an optimised state-of-the-art 2D U-Net model by studying the effects of the individual deep learning model components in performing prostate segmentation. We found that for upsampling, the combination of interpolation and convolution is better than the use of transposed convolution. For combining feature maps in each convolution block, it is only beneficial if a skip connection with concatenation is used. With respect to pooling, average pooling is better than strided-convolution, max, RMS or L2 pooling. Introducing a batch normalisation layer before the activation layer gives further performance improvement. The optimisation is based on a private dataset as it has a fixed 2D resolution and voxel size for every image which mitigates the need of a resizing operation in the data preparation process. Non-enhancing data preprocessing was applied and five-fold cross-validation was used to evaluate the fully automatic segmentation approach. We show it outperforms the traditional methods that were previously applied on the private dataset, as well as outperforming other comparable state-of-the-art 2D models on the public dataset PROMISE12.
- Subjects
PROSTATE; ARTIFICIAL neural networks; DEEP learning; MAGNETIC resonance imaging; MATHEMATICAL convolutions; EXOCRINE glands
- Publication
Applied Sciences (2076-3417), 2020, Vol 10, Issue 7, p2601
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app10072601