Cheema, M., Nazir, A., Yang, P. orcid.org/0000-0002-8553-7127 et al. (7 more authors) (2021) Modified GAN-cAED to minimize risk of unintentional liver major vessels cutting by controlled segmentation using CTA/SPET-CT. IEEE Transactions on Industrial Informatics, 17 (12). pp. 7991-8002. ISSN 1551-3203
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.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Liver vessel segmentation; image synthesis; fused positron emission tomography-computed tomography (PET-CT); synthesized PET-CT (SPET-CT); liver resection |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 12 Mar 2021 08:18 |
Last Modified: | 08 Mar 2022 01:38 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TII.2021.3064369 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:171762 |