ImageJ is extensively used in major areas of biological and material sciences. Previously developed active segmentation platform as a plugin for ImageJ incorporate Weka toolbox-based statistical machine learning algorithms as well as deep learning techniques for trainable image segmentation. The end goal of the active segmentation platform for ImageJ is to provide researchers an extensible toolbox enabling them to select custom filters and machine learning algorithms for their research. Under the existing implementation of the active segmentation platform, it only supports users with a limited way to load the ground truth for learning ( at the moment only as of the region on interest format (ROI)). Thus, this reduced the usability of the tool and it urges the users to convert the ground truth to the specific format which is designed to be used within the application. Therefore the main contributions under this project will be to incorporate several ground-truth formats as image-based in which each pixel uniquely belongs to a particular class, partial ground truth format in which instead of the whole image and several partial boxes in an image or stack are labeled.



Piyumal Demotte


  • Dimiter Prodanov
  • Sumit Kumar Vohra