Joint reconstruction and segmentation of 7T-like MR images from 3T MRI based on cascaded convolutional neural networks

Khosro Bahrami, Islem Rekik, Feng Shi, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

7T MRI scanner provides MR images with higher resolution and better contrast than 3T MR scanners. This helps many medical analysis tasks, including tissue segmentation. However, currently there is a very limited number of 7T MRI scanners worldwide. This motivates us to propose a novel image post-processing framework that can jointly generate high-resolution 7T-like images and their corresponding high-quality 7T-like tissue segmentation maps, solely from the routine 3T MR images. Our proposed framework comprises two parallel components, namely (1) reconstruction and (2) segmentation. The reconstruction component includes the multi-step cascaded convolutional neural networks (CNNs) that map the input 3T MR image to a 7T-like MR image, in terms of both resolution and contrast. Similarly, the segmentation component involves another paralleled cascaded CNNs, with a different architecture, to generate high-quality segmentation maps. These cascaded feedbacks between the two designed paralleled CNNs allow both tasks to mutually benefit from each another when learning the respective reconstruction and segmentation mappings. For evaluation, we have tested our framework on 15 subjects (with paired 3T and 7T images) using a leave-one-out cross-validation. The experimental results show that our estimated 7T-like images have richer anatomical details and better segmentation results, compared to the 3T MRI. Furthermore, our method also achieved better results in both reconstruction and segmentation tasks, compared to the state-of-the-art methods.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
PublisherSpringer Verlag
Pages764-772
Number of pages9
Volume10433 LNCS
ISBN (Print)9783319661810
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 2017 Sep 112017 Sep 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10433 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period17/9/1117/9/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Joint reconstruction and segmentation of 7T-like MR images from 3T MRI based on cascaded convolutional neural networks'. Together they form a unique fingerprint.

  • Cite this

    Bahrami, K., Rekik, I., Shi, F., & Shen, D. (2017). Joint reconstruction and segmentation of 7T-like MR images from 3T MRI based on cascaded convolutional neural networks. In Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings (Vol. 10433 LNCS, pp. 764-772). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10433 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-66182-7_87