Semi-automated

QuantCenter is the framework for 3DHISTECH image analysis applications. with the goal of helping the pathologists to diagnose in an easier way. QuantCenter, is optimized for whole slide quantification. It has a linkable algorithm concept that tries to provide an easy-to-use and logical workflow. The user has different quantification modules that he or she could link one after other to fine-tune or to speed up the analysis.

ASAP is an open source platform for visualizing, annotating and automatically analyzing whole-slide histopathology images. It consists of several key-components (slide input/output, image processing, viewer) which can be used seperately. It is built on top of several well-developed open source packages like OpenSlide, Qt and OpenCV but also tries to extend them in several meaningful ways.

QuPath is open source software for Quantitative Pathology. QuPath has been developed as a research tool at Queen's University Belfast.

This is a Java content-based image retrieval software components. It can be runned independantly or connected to a Cytomine server. Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases. Content-based" means that the search algorithm analyzes the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with the image. The term "content" in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. The CBIRetrieval library is: Incremental: You can add new images all over the time. Scalable: Run as many server as you want. Client performs search on all servers. Flexible: Run as a simple app (command line) or use the JAR in your own JVM app/server (java import) Opensource/Free: Apache 2.0 CBIRetrieval is a java library for CBIR, CBIRest is a server with a REST HTTP API with CBIRetrieval embedded. If you want to connect a software/webapp with a CBIR engine, you should use CBIRest. This is a fast multi-threaded and noSQL implementation of the algorithm published in: Incremental Indexing and Distributed Image Search using Shared Randomized Vocabularies Marée, Raphaël; Denis, Philippe; Wehenkel, Louis; Geurts, Pierre,in ACM Proceedings MIR 2010 (2010, March). It was applied on histology images and radiology images.

This is a learnable segmentation algorithm based on ground-truth images and segmentation mask. It learns a multiple output pixel classification algorithm. It downloads from Cytomine-Core annotation images+alphamasks from project(s), build a segmentation (pixel classifier) model which is saved locally. Typical application: tumor detection in tissues in histology slides. It is based on "Fast Multi-Class Image Annotation with Random Subwindows and Multiple Output Randomized Trees" http://orbi.ulg.ac.be/handle/2268/12205 and was used in "A hybrid human-computer approach for large-scale image-based measurements using web services and machine learning" http://orbi.ulg.ac.be/handle/2268/162084?locale=en