pathology

Collection of several basic standard image segmentation methods focusing on medical imaging. In particular, the key block/applications are (un)supervised image segmentation using superpixels, object centre detection and region growing with a shape prior. Besides the open-source code, there is also a few sample images.

Analysis of adipocyte number and size. The original code and example images supposed to be discovered at http://webspace.buckingham.ac.uk/klanglands/ but currently the webpage is missing the code and sample images.

SLDC is an open-source Python workflow. SLDC stands for Segment Locate Dispatch Classify. This framework aims at facilitating the development of algorithms for detecting objects in multi-gigapixel images. Particularly, it provides algorithm developers with a structure to define problem-dependent components of their processing workflow (i.e. segmentation and classification) in a concise way. Every other concern such as parallelization and large image handling are encapsulated by the framework. It also features a powerful and customizable logging system and some components to apply several workflows one after another on a same image. SLDC can work on local images or interact with Cytomine

Example image:

Toy image data

[no download link, this description itself explains the steps to quantify staining in tissue sections] The Color Deconvolution plugin for ImageJ can be used to digitally separate up to three stains from brightfield images, after which standard ImageJ commands can be used. The algorithm is described in Ruifork and Johnston (2001). **However**, it is **very** important to take into consideration the caveats on the linked URL. In particular, note that: - Stain colors depend on numerous factors, such as the precise stains and scanner; therefore, the 'default' stain vectors (used to define the colors) are unlikely to be optimal and may be very inaccurate. See the URL instructions for how to create new stain vectors. - Pixel values should be interpreted with extreme caution; in particular, note the warning regarding 'brown' staining that *attempting to quantify DAB intensity using this plugin is not a good idea*. Note, the pixel values provided by this plugin are 8-bit and **not** equivalent to 'optical densities' frequently presented in the literature. Color deconvolution is particularly helpful in separating stains so that stained regions can be detected (e.g. by setting a threshold), and then the number or areas of stained structures may be quantified. Two potential approaches would be: 1. If one measurement should be made for the entire image: - *Image > Adjust > Threshold...* - *Edit > Selection > Create Selection* - *Analyze > Measure* 2. If distinct structures should be measured: - *Image > Adjust > Threshold...* - *Analyze > Analyze Particles...*