Cross-modality knowledge transfer for prostate segmentation from CT scans

Yucheng Liu*, Naji Khosravan, Yulin Liu, Joseph Stember, Jonathan Shoag, Ulas Bagci, Sachin Jambawalikar

*Corresponding author for this work

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

7 Scopus citations

Abstract

Creating large scale high-quality annotations is a known challenge in medical imaging. In this work, based on the CycleGAN algorithm, we propose leveraging annotations from one modality to be useful in other modalities. More specifically, the proposed algorithm creates highly realistic synthetic CT images (SynCT) from prostate MR images using unpaired data sets. By using SynCT images (without segmentation labels) and MR images (with segmentation labels available), we have trained a deep segmentation network for precise delineation of prostate from real CT scans. For the generator in our CycleGAN, the cycle consistency term is used to guarantee that SynCT shares the identical manually-drawn, high-quality masks originally delineated on MR images. Further, we introduce a cost function based on structural similarity index (SSIM) to improve the anatomical similarity between real and synthetic images. For segmentation followed by the SynCT generation from CycleGAN, automatic delineation is achieved through a 2.5D Residual U-Net. Quantitative evaluation demonstrates comparable segmentation results between our SynCT and radiologist drawn masks for real CT images, solving an important problem in medical image segmentation field when ground truth annotations are not available for the modality of interest.

Original languageEnglish (US)
Title of host publicationDomain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data First MICCAI Workshop, DART 2019 and First International Workshop, MIL3ID 2019 Shenzhen, Held in Conjunction with MICCAI 2019 Shenzhen, 2019 Proceedings
EditorsQian Wang, Fausto Milletari, Nicola Rieke, Hien V. Nguyen, Badri Roysam, Shadi Albarqouni, M. Jorge Cardoso, Ziyue Xu, Konstantinos Kamnitsas, Vishal Patel, Steve Jiang, Kevin Zhou, Khoa Luu, Ngan Le
PublisherSpringer
Pages63-71
Number of pages9
ISBN (Print)9783030333904
DOIs
StatePublished - 2019
Externally publishedYes
Event1st MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the 1st International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer- Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 13 2019Oct 17 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11795 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the 1st International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer- Assisted Intervention, MICCAI 2019
Country/TerritoryChina
CityShenzhen
Period10/13/1910/17/19

Keywords

  • 2.5D
  • CT synthesis
  • Deep learning
  • Domain adaptation
  • Generative Adversarial Networks
  • Prostate segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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