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, embryos) show that the precision of the segmentation reaches the level of a human expert.
Tasnadi E.A., Toth T., Kovacs M., Diosdi A., Pampaloni F., Molnar J., et al. (2020). 3D-Cell-Annotator: an open-source active surface tool for single-cell segmentation in 3D microscopy images. BIOINFORMATICS, 36(9), 2948-2949 [10.1093/bioinformatics/btaa029].
3D-Cell-Annotator: an open-source active surface tool for single-cell segmentation in 3D microscopy images
Piccinini F.;
2020
Abstract
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, embryos) show that the precision of the segmentation reaches the level of a human expert.File | Dimensione | Formato | |
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0050_2020_BioInformatics_Tasnadi.pdf
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btaa029_supplementary_data.pdf
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