Segmenting new image acquisitions without labels

Timothy L. Kline

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


We are interested in solving the problem of segmentation when no gold-standard labels are available for new image acquisition protocols. We developed a dual generative adversarial network (GAN), called Synth-GAN, which incorporates a differential operator loss (to favor retaining edges), as well as cyclic loss (to guarantee reconstruction of inputs). We show how the developed approach facilitates the application of an automated deep learning approach trained on one type of image (T2-weighted fat-sat MR) to be successfully applied to images well outside the trained distribution (Tl-weighted MR). A total of 100 images of each sequence from different patients were used (80% for training), and performance of the method was assessed by comparing how the previously developed automated segmentation approach performed prior to and post application of Synth-GAN. The developed approach improved the DICE coefficient from 0.39 (applying the automated segmentation method to the original Tl images) to 0.74 (applying the segmentation method to the synthesized T2 images). This approach will be useful for generalizing automated approaches across modalities, and institutions, when differences in hardware and software significantly alter image representations.

Original languageEnglish (US)
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9781538636411
StatePublished - Apr 2019
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: Apr 8 2019Apr 11 2019

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019


  • Deep learning
  • Generative adversarial networks
  • Loss functions
  • Polycystic kidney disease
  • Total kidney volume

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'Segmenting new image acquisitions without labels'. Together they form a unique fingerprint.

Cite this