mouse

Algorithm and software created to extract animal trajectories from videos of a collection of animals up to 100 individuals. Idtrackerai uses two convolutional networks: one for animal identification and another to detect when animals touch or cross each other.

This workflow describes a deep-learning based pipeline for reliable single-organoid segmentation and tracking in 2D+t high-resolution brightfield microscopy of mouse mammary epithelial organoids. The pipeline involves a four-layer U-Net to infer semantic segmentation predictions, adaptive morphological filtering to establish candidate organoid instances, and a shape-similarity-constrained, instance-segmentation-correcting tracking step to associate the corresponding organoid instances in time.

It is particularly focused on automatically detecting an organoid located approximately in the center of the first frame and track all its subsequent instances in the remaining frames, emphasizing on accurate organoid boundary delineation. Furthermore, segmentation network was trained using plausible pix2pixHD-generated bioimage data. Syntheric image simulator code and data are also available here.

This python toolbox performs registration between 2-D microscopy images from the same tissue section or serial sections in several ways to achieve imaging mass spectrometry (IMS) experimental goals.

This code supports the following works and enables others to perform the workflows outlined in the following works, please cite them if you use this toolbox:

• Advanced Registration and Analysis of MALDI Imaging Mass Spectrometry Measurements through Autofluorescence Microscopy, 10.1021/acs.analchem.8b02884

• Next Generation Histology-directed Imaging Mass Spectrometry Driven by Autofluorescence Microscopy, 10.1021/acs.analchem.8b02885