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  • Algorithm & details
    You can find information about LesionSeg3D in this paper 

Hoogi A, Subramaniam A, Veerapaneni R, Rubin DL. Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis. IEEE Trans Med Imaging. 2017 Mar;36(3):781-791. doi: 10.1109/TMI.2016.2628084. Epub 2016 Nov 11. PMID: 28113927; PMCID: PMC5510759.

  • docker Information
    We have created an image that reads DICOMs, and an AIM file with seed annotation and creates a DICOM Segmentation Object with the lesion
    you can pull the image with : docker pull rubinlab/lesionseg3d
  • How to use in ePad
    • Register the plugin
    • Select annotations that has a line (long axis preferably in the middle of the lesion) and a comment with the first slice instance number and last start instance number separated with (ex: 120-158)