Ensemble methods and partially-supervised learning for accurate and robust automatic murine organ segmentation
Abstract Delineation of multiple organs in murine µCT images is crucial for preclinical studies but requires manual volumetric segmentation, a tedious and time-consuming process prone to inter-observer variability. Automatic deep learning-based segmentation can improve speed and reproducibility. Whi...
Saved in:
| Main Authors: | Lars H. B. A. Daenen, Joël de Bruijn, Nick Staut, Frank Verhaegen |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-05954-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Robust Segmentation of Partial and Imperfect Dental Arches
by: Ammar Alsheghri, et al.
Published: (2024-11-01) -
Towards Robust Supervised Pectoral Muscle Segmentation in Mammography Images
by: Parvaneh Aliniya, et al.
Published: (2024-12-01) -
Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.
by: Jun Yi Wang, et al.
Published: (2016-01-01) -
MEF-AlloSite: an accurate and robust Multimodel Ensemble Feature selection for the Allosteric Site identification model
by: Sadettin Y. Ugurlu, et al.
Published: (2024-10-01) -
Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images
by: Shih-Sheng Chang, et al.
Published: (2024-03-01)