Image segmentation

Image segmentation is (one of) the (few) concept(s) on the border between Image (pre)processing (Image->Image) and Image analysis (Image->Data).

Description

The empanada-napari plugin is built to democratize deep learning image segmentation for researchers in electron microscopy (EM). It ships with MitoNet, a generalist model for the instance segmentation of mitochondria. There are also tools to quickly build and annotate training datasets, train generic panoptic segmentation models, finetune existing models, and scalably run inference on 2D or 3D data. To make segmentation model training faster and more robust, CEM pre-trained weights are used by default. These weights were trained using an unsupervised learning algorithm on over 1.5 million EM images from hundreds of unique EM datasets making them remarkably general.

Empanada-napari

MIA

Description

ModularImageAnalysis (MIA) is an ImageJ plugin which provides a modular framework for assembling image and object analysis workflows. Detected objects can be transformed, filtered, measured and related. Analysis workflows are batch-enabled by default, allowing easy processing of high-content datasets.

MIA is designed for “out-of-the-box” compatibility with spatially-calibrated 5D images, yielding measurements in both pixel and physical units.  Functionality can be extended both internally, via integration with SciJava’s scripting interface, and externally, with Java modules that extend the MIA framework. Both have full access to all objects and images in the analysis workspace.

Workflows are, by default, compatible with batch processing multiple files within a single folder. Thanks to Bio-Formats, MIA has native support for multi-series image formats such as Leica .lif and Nikon .nd2.

Workflows can be automated from initial image loading through processing, object detection, measurement extraction, visualisation, and data exporting. MIA includes near 200 modules integrated with key ImageJ plugins such as Bio-Formats, TrackMate and Weka Trainable Segmentation.

Module(s) can be turned on/off dynamically in response to factors such as availability of images and objects, user inputs and measurement-based filters. Switches can also be added to “processing view” for easy workflow control.

MIA is developed in the Wolfson Bioimaging Facility at the University of Bristol.

Description

Machine Learning made easy

APEER ML provides an easy way to train your own machine learning
models and segment your microscopy images. No expertise or coding required.

APEER

Image Analysis Training Resources

Submitted by Perrine on Wed, 06/30/2021 - 14:15

This is a resource for image analysis training material, with a focus on research in the life sciences.

Currently, this resource is mainly meant to serve image analysis trainers, helping them to design courses. However, we might add more text (or videos) to the material such that it could also be used by students for self-directed study.

Description

This tool allows to analyze morphological characteristics of complex roots. While for young roots the root system architecture can be analyzed automatically, this is often not possible for more developed roots. The tool is inspired by the Sholl analysis used in neuronal studies. The tool creates a binary mask and the Euclidean Distance Transform from the input image. It then allows to draw concentric circles around a base point and to extract measures on or within the circles. Instead of circles, which present the distance from the base point, horizontal lines can be used, which present the distance in the soil from the base-line. The following features are currently implemented:

  • The area of the root per distance/depth.
  • The number of border pixel per distance/depth, giving an idea of the surface in contact with the soil.
  • The maximum radius per distance/depth of a root, measured at the crossing points with the circles or lines.
  • The number of crossings of roots with the circles or lines.
  • The maximum distance to the left and the right from the vertical axis at crossing points with the circles or lines.
Concentric circles on the mask of a root, created by the Analyze Complex Roots Tool