MIPAV

Bio Image Analyst
Developper

The MIPAV (Medical Image Processing, Analysis, and Visualization) application enables quantitative analysis and visualization of medical images of numerous modalities such as PET, MRI, CT, or microscopy. Using MIPAV's standard user-interface and analysis tools, researchers at remote sites (via the internet) can easily share research data and analyses, thereby enhancing their ability to research, diagnose, monitor, and treat medical disorders.

2D brain slice region annotation: SliceMap

Developper
Bio Image Analyst

SliceMap

Whole brain tissue slices are commonly used in neurobiological research for analyzing pathological features in an anatomically defined manner. However, since many pathologies are expressed in specific regions of the brain, it is necessary to have an annotation of the regions in the brain slices. Such an annotation can be done by manual delineation, as done most often, or by an automated region annotation tool.

SuRVoS

Bio Image Analyst
microscopist

SuRVoS: Super-Region Volume Segmentation workbench

A volume is first partitioned into Super-Regions (superpixels or supervoxels) and then interactively segmented by the user providing training annotations. SuRVoS can then learn from and extend the annotations to the whole volume.

Simple Tracing DF-Tracing

Bio Image Analyst

We have developed a novel approach, named DF-Tracing, to tackle this challenge. This method first extracts the neurite signal (foreground) from a noisy image by using anisotropic filtering and automated thresholding. Then, DF-Tracing executes a coupled distance-field (DF) algorithm on the extracted foreground neurite signal and reconstructs the neuron morphology automatically.

Spot Detector

Bio Image Analyst
Developper

Spot detector detects and counts spots, based on wavelet transform.

- Detects spots in noisy images 2D/3D.
- Depending on objective, spots can be nuclei, nucleus or cell
- Versatile input: sequence or batch of file.
- Detects spot in specific band/channel.
- Multi band labeling: automaticaly creates ROIs from one band and count in the same or an other band.
- Filters detection by size.
- Sort detection by ROIs
- Output data in XLS Excel files: number of detection by ROIs, and each detection location and size.