Modified GAN-cAED to minimize risk of unintentional liver major vessels cutting by controlled segmentation using CTA/SPET-CT

Muhammad Nadeem Cheema, Anam Nazir, Po Yang, Bin Sheng, Ping Li, Huating Li, Xiaoer Wei, Jing Qin, Jinman Kim, David Dagan Feng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

This paper substantially advances upon state-of-the-art to enhance liver vessels segmentation accuracy by leveraging advantages of synthetic PET-CT (SPET-CT) images in addition to computed tomography angiography (CTA) volumes. Our setup makes a hybrid solution of modified GAN-cAED combining synthetic ability of generative adversarial network (GAN) to deliver SPET-CT images with generative ability of convolutional autoencoder (cAED) network in terms of latent learning to more refined segmentation of major liver vessels. We improve time complexity through a novel concept of controlled segmentation by introducing a threshold metric to stop segmentation up-to a desired level. The innovative concept of controlled vessel segmentation with a stopping criterion via variant threshold levels will help surgeons to avoid unintentional major blood vessels cutting, reducing the risk of excessive blood loss. Clinically, such solutions offer computer-aided liver surgeries and drug treatment evaluation in a CTA-only environment, shorten the requirement of radioactive and expensive fused PET-CT images.

Original languageEnglish
Pages (from-to)7991-8002
Number of pages12
JournalIEEE Transactions on Industrial Informatics
Volume17
Issue number12
DOIs
Publication statusPublished - Dec 2021

Keywords

  • Computed tomography
  • fused positron emission tomography-computed tomography (PET-CT)
  • Generative adversarial networks
  • Image segmentation
  • image synthesis
  • Informatics
  • Liver
  • liver resection
  • Liver vessel segmentation
  • Measurement
  • Surgery
  • synthesized PET-CT (SPET-CT)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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