{"id":1770,"date":"2016-09-14T11:28:53","date_gmt":"2016-09-14T10:28:53","guid":{"rendered":"http:\/\/wp.cs.ucl.ac.uk\/gift-surg\/?page_id=1770"},"modified":"2016-09-14T11:28:53","modified_gmt":"2016-09-14T10:28:53","slug":"software","status":"publish","type":"page","link":"https:\/\/wp1.cs.ucl.ac.uk\/gift-surg\/project\/software\/","title":{"rendered":"Open-Source Software"},"content":{"rendered":"

GIFT-Surg Software<\/h1>\n

<\/a>Slic-Seg<\/h2>\n

Slic-Seg is open-source software for the interactive segmentation of medical images. Slic-Seg provides a prototype tool that could be used to help identify and analyze organs such as the placenta as part of assessment and treatment planning for fetal surgery and other surgical applications.<\/p>\n

Slic-Seg is a minimally interactive online learning-based segmentation method. An online random forest is first trained on data coming from scribbles provided by the user in one single selected start slice. This forms the basis for a slice-by-slice framework that segments subsequent slices before incorporating them into the training set on the fly.<\/p>\n

Slic-Seg was developed by Guotai Wang at the Translational Imaging Group<\/a> at UCL.<\/p>\n

Download Slic-Seg<\/a> (GitHub)<\/p>\n

<\/h1>\n

<\/a>DyBa ORF<\/h2>\n

DyBa ORF is a Dymanically Balanced Online Random Forest, which addresses three problems in learning with Random Forests:<\/p>\n