3D-Cell-Annotator

3D-Cell-Annotator performs single-cell segmentation in three-dimensional microscopy images using active surface models and shape-based priors to delineate cellular structures.

Key Features:

• 3D Active Surface Segmentation: Uses three-dimensional active surface models that evolve to fit the contours of cells in volumetric microscopy data.
• Shape Descriptor Integration: Incorporates morphological shape descriptors as prior information to improve segmentation accuracy for densely packed or overlapping cells.
• GPU-Accelerated Implementation: Implements segmentation algorithms in CUDA/C++ to utilize NVIDIA GPU parallel processing for high-performance computation.
• MITK Platform Integration: Operates as a plugin within the Medical Imaging Interaction Toolkit (MITK) to support analysis of three-dimensional biomedical imaging datasets.

Scientific Applications:

• 3D Cell Segmentation: Enables segmentation of individual cells in three-dimensional microscopy datasets.
• Organoid and Spheroid Analysis: Supports quantitative cellular analysis in complex 3D biological systems such as spheroids, organoids, and embryos.
• Training Data Generation for Machine Learning: Facilitates creation of annotated datasets for developing deep learning models in bioimage analysis.

Methodology:

The method applies semi-automated three-dimensional active surface models that iteratively evolve to fit cell boundaries in microscopy volumes while incorporating shape descriptors as prior constraints to guide segmentation.