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2019 | OriginalPaper | Chapter

Cross-Modality Knowledge Transfer for Prostate Segmentation from CT Scans

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

Published in: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

Publisher: Springer International Publishing

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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.
Literature
1.
go back to reference Nordstrm, T., et al.: Prostate-specific antigen (PSA) density in the diagnostic algorithm of prostate cancer. Prostate Cancer Prostatic Dis. 21(1), 57–63 (2017) CrossRef Nordstrm, T., et al.: Prostate-specific antigen (PSA) density in the diagnostic algorithm of prostate cancer. Prostate Cancer Prostatic Dis. 21(1), 57–63 (2017) CrossRef
2.
go back to reference Smith, W.L., et al.: Prostate volume contouring: a 3D analysis of segmentation using 3DTRUS, CT, and MR. Int. J. Radiat. Oncol. Biol. Phys. 67(4), 1238–1247 (2007) CrossRef Smith, W.L., et al.: Prostate volume contouring: a 3D analysis of segmentation using 3DTRUS, CT, and MR. Int. J. Radiat. Oncol. Biol. Phys. 67(4), 1238–1247 (2007) CrossRef
3.
go back to reference Rasch, C., et al.: Definition of the prostate in CT and MRI: a multi-observer study. Int. J. Radiat. Oncol. Biol. Phys. 43(1), 57–66 (1999) CrossRef Rasch, C., et al.: Definition of the prostate in CT and MRI: a multi-observer study. Int. J. Radiat. Oncol. Biol. Phys. 43(1), 57–66 (1999) CrossRef
4.
go back to reference Chowdhury, N., et al.: Concurrent segmentation of the prostate on MRI and CT via linked statistical shape models for radiotherapy planning. Med. Phys. 39(4), 2214–2228 (2012) CrossRef Chowdhury, N., et al.: Concurrent segmentation of the prostate on MRI and CT via linked statistical shape models for radiotherapy planning. Med. Phys. 39(4), 2214–2228 (2012) CrossRef
5.
go back to reference Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv:​1703.​10593 (2017) Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv:​1703.​10593 (2017)
11.
go back to reference Zhao, H., et al.: Loss functions for image restoration with neural networks. IEEE Trans. Comput. Imaging 3(1), 47–57 (2017) CrossRef Zhao, H., et al.: Loss functions for image restoration with neural networks. IEEE Trans. Comput. Imaging 3(1), 47–57 (2017) CrossRef
12.
go back to reference Liu, C., et al.: Automatic segmentation of the prostate on CT images using deep neural networks (DNN). Int. J. Radiat. Oncol. Biol. Phys. 104(4), 924–932 (2019) CrossRef Liu, C., et al.: Automatic segmentation of the prostate on CT images using deep neural networks (DNN). Int. J. Radiat. Oncol. Biol. Phys. 104(4), 924–932 (2019) CrossRef
13.
go back to reference Burgos, N., et al.: Iterative framework for the joint segmentation and CT synthesis of MR images: application to MRI-only radiotherapy treatment planning. Phys. Med. Biol. 62, 4237–4253 (2017) CrossRef Burgos, N., et al.: Iterative framework for the joint segmentation and CT synthesis of MR images: application to MRI-only radiotherapy treatment planning. Phys. Med. Biol. 62, 4237–4253 (2017) CrossRef
Metadata
Title
Cross-Modality Knowledge Transfer for Prostate Segmentation from CT Scans
Authors
Yucheng Liu
Naji Khosravan
Yulin Liu
Joseph Stember
Jonathan Shoag
Ulas Bagci
Sachin Jambawalikar
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-33391-1_8

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