Unsupervised medical image registration using cycle-consistent convolutional neural network = 주기적 일관성의 합성곱 신경망을 이용한 비지도 학습 기반의 의료영상 정합 연구

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 15
  • Download : 0
Image registration is one of the key processing steps for biomedical image analysis such as diagnosis of disease. Recently, deep learning based supervised and unsupervised image registration methods have been extensively studied due to its excellent performance in spite of ultra-fast computational time compared to the classical approaches. Most of the deep learning algorithms are designed to ensure diffeomorphism for the registration in calculating the deformation vector field. In this paper, based on the observation that a homeomorphic mapping between two topological spaces is as powerful as a diffeomorphism in ensuring the topology preservation and one-to-one mapping, we present a novel unsupervised medical image registration method that trains deep neural network to deform a 3D volume by homeomorphic mapping using a cycle-consistency. Using difficult multiphase liver registration tasks, we evaluate target registration error for the deformed images and demonstrate that the proposed method can provide accurate 3D image registration within a few seconds.
Advisors
Ye, Jong Chulresearcher예종철researcher
Description
한국과학기술원 :바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2019.2,[v, 38 p. :]

Keywords

medical image registration▼aunsupervised learning▼adeep learning▼acycle consistency; 의료영상 정합▼a비지도 학습▼a심층 학습▼a주기적 일관성

URI
http://hdl.handle.net/10203/266174
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843111&flag=dissertation
Appears in Collection
BiS-Theses_Master(석사논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0