A framework for feedback-based segmentation of 3D image stacks
Abstract: 3D segmentation has become a widely used technique. However, automatic segmentation does not deliver high accuracy in optically dense images and manual segmentation lowers the throughput drastically. Therefore, we present a workflow for 3D segmentation being able to forecast segments based on a user-given ground truth. We provide the possibility to correct wrong forecasts and to repeatedly insert ground truth in the process. Our aim is to combine automated and manual segmentation and therefore to improve accuracy by a tunable amount of manual input.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
A framework for feedback-based segmentation of 3D image stacks ; volume:2 ; number:1 ; year:2016 ; pages:437-441 ; extent:5
Current directions in biomedical engineering ; 2, Heft 1 (2016), 437-441 (gesamt 5)
- Creator
-
Stegmaier, Johannes
Peter, Nico
Portl, Julia
Mang, Ira V.
Schröder, Rasmus
Leitte, Heike
Mikut, Ralf
Reischl, Markus
- DOI
-
10.1515/cdbme-2016-0097
- URN
-
urn:nbn:de:101:1-2410141658047.270762431536
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:21 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Stegmaier, Johannes
- Peter, Nico
- Portl, Julia
- Mang, Ira V.
- Schröder, Rasmus
- Leitte, Heike
- Mikut, Ralf
- Reischl, Markus
Other Objects (12)
A robust and consistent stack generalized ensemble-learning framework for image segmentation
Multi-level image segmentation with internal feedback mechanisms
Segmentation, Tracing, and Quantification of Microglial Cells from 3D Image Stacks
Cell image segmentation by using feedback and convolutional LSTM
High throughput automatic muscle image segmentation using parallel framework
Seg2Link: an efficient and versatile solution for semi-automatic cell segmentation in 3D image stacks
Multiresolution image segmentation
A point cloud segmentation framework for image-based spatial transcriptomics
A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems
A framework for biomedical figure segmentation towards image-based document retrieval
Buildtest: HPC Software Stack Testing Framework
Sequential interactive image segmentation
A robust and consistent stack generalized ensemble-learning framework for image segmentation
Multi-level image segmentation with internal feedback mechanisms
Segmentation, Tracing, and Quantification of Microglial Cells from 3D Image Stacks
Cell image segmentation by using feedback and convolutional LSTM
High throughput automatic muscle image segmentation using parallel framework
Seg2Link: an efficient and versatile solution for semi-automatic cell segmentation in 3D image stacks
Multiresolution image segmentation
A point cloud segmentation framework for image-based spatial transcriptomics
A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems
A framework for biomedical figure segmentation towards image-based document retrieval
Buildtest: HPC Software Stack Testing Framework
Sequential interactive image segmentation
A robust and consistent stack generalized ensemble-learning framework for image segmentation
Multi-level image segmentation with internal feedback mechanisms
Segmentation, Tracing, and Quantification of Microglial Cells from 3D Image Stacks
Cell image segmentation by using feedback and convolutional LSTM
High throughput automatic muscle image segmentation using parallel framework
Seg2Link: an efficient and versatile solution for semi-automatic cell segmentation in 3D image stacks
Multiresolution image segmentation
A point cloud segmentation framework for image-based spatial transcriptomics
A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems
A framework for biomedical figure segmentation towards image-based document retrieval
Buildtest: HPC Software Stack Testing Framework