Liver lesion detection in CT using deep learning techniques

Avi Ben-Cohen, Hayit Greenspan

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Computer aided diagnosis (CAD) tools have the potential to support the radiologists in detection and can lead to improved diagnosis. This chapter presents deep learning based techniques for automated liver lesion analysis in computed tomography (CT) images. In the first part of this chapter we focus on the detection of liver metastases using both a global context with a fully convolutional network (FCN), and a local patch level analysis with a superpixel sparse based classification. In the second part of this chapter we present a novel system for the generation of virtual PET images using CT scans. We combine an FCN with a conditional generative adversarial network (GAN) to generate simulated PET data from given input CT data and show its benefit by reducing the false-positive rate.

Original languageEnglish
Title of host publicationHandbook of Medical Image Computing and Computer Assisted Intervention
Number of pages26
ISBN (Electronic)9780128161760
StatePublished - 1 Jan 2019


  • Conditional GAN
  • Cross-modality
  • Detection
  • FCN
  • Liver Lesions


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