AI Article Synopsis
- Segmentation of single cells in microscopy images is critical for bioimage analysis and is addressed by the 3D-Cell-Annotator, which combines 3D active surfaces and shape descriptors for semi-automated segmentation.
- The tool is integrated into the Medical Imaging Interaction Toolkit (MITK) and achieves segmentation precision comparable to human experts on various 3D biological structures.
- 3D-Cell-Annotator is available for download as a CUDA/C++ patch for MITK, compatible with Windows 64-bit and recent Linux systems, and can run on consumer-level laptops with a CUDA-enabled NVIDIA graphics card.
Article Abstract
Summary: Segmentation of single cells in microscopy images is one of the major challenges in computational biology. It is the first step of most bioimage analysis tasks, and essential to create training sets for more advanced deep learning approaches. Here, we propose 3D-Cell-Annotator to solve this task using 3D active surfaces together with shape descriptors as prior information in a semi-automated fashion. The software uses the convenient 3D interface of the widely used Medical Imaging Interaction Toolkit (MITK). Results on 3D biological structures (e.g. spheroids, organoids and embryos) show that the precision of the segmentation reaches the level of a human expert.
Availability And Implementation: 3D-Cell-Annotator is implemented in CUDA/C++ as a patch for the segmentation module of MITK. The 3D-Cell-Annotator enabled MITK distribution can be downloaded at: www.3D-cell-annotator.org. It works under Windows 64-bit systems and recent Linux distributions even on a consumer level laptop with a CUDA-enabled video card using recent NVIDIA drivers.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7203751 | PMC |
| http://dx.doi.org/10.1093/bioinformatics/btaa029 | DOI Listing |
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