Ervin A Tasnadi1, Timea Toth1, Maria Kovacs1, Akos Diosdi1, Francesco Pampaloni2, Jozsef Molnar1, Filippo Piccinini3, Peter Horvath1,4,5. 1. Synthetic and System Biology Unit, Biological Research Centre (BRC), Szeged H-6726, Hungary. 2. Buchmann Institute for Molecular Life Sciences (BMLS), Goethe University of Frankfurt, DE-60438 Frankfurt, Germany. 3. Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, I-47014 Meldola (FC), Italy. 4. Institute for Molecular Medicine Finland University of Helsinki, FI-00014 Helsinki, Finland. 5. Single-Cell Technologies Ltd, H-6726 Szeged, Hungary.
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.
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.
Authors: Guorong Wu; Jason L Stein; David Borland; Carolyn M McCormick; Niyanta K Patel; Oleh Krupa; Jessica T Mory; Alvaro A Beltran; Tala M Farah; Carla F Escobar-Tomlienovich; Sydney S Olson; Minjeong Kim Journal: BMC Bioinformatics Date: 2021-05-22 Impact factor: 3.169
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