Contents

Quantitative Criterion Acquisition Network (QCA Net) performs instance segmentation of 3D fluorescence microscopic images. QCA Net consists of Nuclear Segmentation Network (NSN) that learned nuclear segmentation task and Nuclear Detection Network (NDN) that learned nuclear identification task. QCA Net performs instance segmentation of the time-series 3D fluorescence microscopic images at each time point, and the quantitative criteria for mouse development are extracted from the acquired time-series segmentation image.

LibTIFF - TIFF Library and Utilities. This software provides support for the Tag Image File Format (TIFF), a widely used format for storing image data. libtiff is a library, for reading and writing TIFF, a small collection of tools for doing simple manipulations of TIFF images.

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

Manual tracking using Trackmate plugin (comes with FIji, so no installation required if you are using Fiji). 

This note presents the design of a scalable software package named ImagePy for analysing biological images. Our contribution is concentrated on facilitating extensibility and interoperability of the software through decoupling the data model from the user interface. Especially with assistance from the Python ecosystem, this software framework makes modern computer algorithms easier to be applied in bioimage analysis.

Python is a programming language.

JAMA is a basic linear algebra package for Java. It provides user-level classes for constructing and manipulating real, dense matrices. It is meant to provide sufficient functionality for routine problems, packaged in a way that is natural and understandable to non-experts.

This method was originally designed to track objects (not necessarily spots) already identified in 2D 
frames and has been applied previously to particle tracking and analysis in high-speed atomic force microscopy image series.

An ImageJ/Fiji macro which measures quality through two stacks of images assumed to be acquired from two opposite angle of views using gray-level standard deviation.

BM3D is a recent denoising method based on the fact that an image has a locally sparse representation in transform domain. This sparsity is enhanced by grouping similar 2D image patches into 3D groups. In this paper we propose an open-source implementation of the method. We discuss the choice of all parameter methods and confirm their actual optimality. The description of the method is rewritten with a a more transparent notation that in the original paper. A final index gives nonetheless the correspondence between the new notation and the original notation.

It simulates a three-dimensional ground-truth that resembles aspects of a biological object like a spheroid. For each simulated view the signal is attenuated, convolved with an effective PSF, and anisotropically sampled using a Poisson process. 

This Matlab code simulates the noise of the confocal laser scanning microscope depending on the depth in the image stack (serial sections). Using the stack of binary images, it applies different levels of noise in the signal and background parts of the images to simulate confocal images. This is useful for generating "virtual ground truth" images with known values of sample rotation and distortion.