Exploring automatic liver tumor segmentation using deep learning

J.G. Fernandez, V. Fortunati, S. Mehrkanoon*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

The segmentation of liver tumors is crucial for diagnosis, treatment planning and treatment evaluation. Due to the setbacks that the manual segmentation brings, automatic segmentation has recently gained a lot of attention. In this work, we explore various deep learning based approaches to address automatic liver tumor segmentation. We use the data from the Liver Tumor Segmentation challenge (LiTS). In particular, the considered models here are UNet-based architectures. In addition, we investigate the influence of incorporating extra elements to the pipeline such as attention mechanisms, model ensemble, test-time inference as well as an additional model to reject false positives, over the final performance. The obtained results show that the 3D-UNet architecture, together with ensemble learning methods, performs more accurate predictions than the other examined approaches.
Original languageEnglish
Title of host publication2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
PublisherIEEE
Number of pages8
ISBN (Print)9780738133669
DOIs
Publication statusPublished - 2021
EventInternational Joint Conference on Neural Networks - Online, United States
Duration: 18 Jul 202122 Jul 2021
https://www.ijcnn.org/

Conference

ConferenceInternational Joint Conference on Neural Networks
Abbreviated titleIJCNN 2021
Country/TerritoryUnited States
Period18/07/2122/07/21
Internet address

Keywords

  • Medical imaging
  • Liver tumor segmentation
  • Deep learning
  • Convolutional neural network
  • U-Net

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