An Improved Convolutional Neural Network for 3D Unsupervised Medical Image Registration

Gangcheng Cai, Jiajun Wang, Zheru Chi

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

In recent years, convolutional neural networks have been widely used in medical image registration. VTN network has been the most popular architecture in medical registration community. Although the structure has outstanding overall performance in medical image registration, the VTN architecture seems to own drawbacks in focusing on the areas of interest and extracting features efficiently. Therefore, we propose an improved version called CIRVTN based on the traditional VTN model where global Residual paths, the Inception module and the CBAM attention module are introduced. The introduction of global Residual path can not only alleviate the problem of gradient disappearance, but also improve the reusability of medical image features. Upon introducing the Inception and CBAM attention modules, the adaptability of the network to the diversities in shapes and locations of tissue contents in medical images are improved. Extensive experiments on three professional medical liver datasets showed that the network proposed in this paper outperforms the traditional VTN both in Dice score and computing efficiency.

Original languageEnglish
Title of host publication2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1908-1914
Number of pages7
ISBN (Electronic)9781728186351
DOIs
Publication statusPublished - 11 Dec 2020
Event6th IEEE International Conference on Computer and Communications, ICCC 2020 - Chengdu, China
Duration: 11 Dec 202014 Dec 2020

Publication series

Name2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020

Conference

Conference6th IEEE International Conference on Computer and Communications, ICCC 2020
Country/TerritoryChina
CityChengdu
Period11/12/2014/12/20

Keywords

  • convolutional neural network
  • medical image
  • registration
  • unsupervised

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Information Systems and Management

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