Unpaired synthetic image generation in radiology using GANs

Denis Prokopenko, Joël Valentin Stadelmann, Heinrich Schulz, Steffen Renisch, Dmitry V. Dylov

    Результат исследований: Глава в книге, отчете, сборнике статейМатериалы для конференциирецензирование

    8 Цитирования (Scopus)

    Аннотация

    In this work, we investigate approaches to generating synthetic Computed Tomography (CT) images from the real Magnetic Resonance Imaging (MRI) data. Generating the radiological scans has grown in popularity in the recent years due to its promise to enable single-modality radiotherapy planning in clinical oncology, where the co-registration of the radiological modalities is cumbersome. We rely on the Generative Adversarial Network (GAN) models with cycle consistency which permit unpaired image-to-image translation between the modalities. We also introduce the perceptual loss function term and the coordinate convolutional layer to further enhance the quality of translated images. The Unsharp masking and the Super-Resolution GAN (SRGAN) were considered to improve the quality of synthetic images. The proposed architectures were trained on the unpaired MRI-CT data and then evaluated on the paired brain dataset. The resulting CT scans were generated with the mean absolute error (MAE), the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) scores of 60.83 HU, 17.21 dB, and 0.8, respectively. DualGAN with perceptual loss function term and coordinate convolutional layer proved to perform best. The MRI-CT translation approach holds potential to eliminate the need for the patients to undergo both examinations and to be clinically accepted as a new tool for radiotherapy planning.

    Язык оригиналаАнглийский
    Название основной публикацииArtificial Intelligence in Radiation Therapy - 1st International Workshop, AIRT 2019, Held in Conjunction with MICCAI 2019, Proceedings
    РедакторыDan Nguyen, Steve Jiang, Lei Xing
    ИздательSpringer
    Страницы94-101
    Число страниц8
    ISBN (печатное издание)9783030324858
    DOI
    СостояниеОпубликовано - 2019
    Событие1st International Workshop on Connectomics in Artificial Intelligence in Radiation Therapy, AIRT 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, Китай
    Продолжительность: 17 окт. 201917 окт. 2019

    Серия публикаций

    НазваниеLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Том11850 LNCS
    ISSN (печатное издание)0302-9743
    ISSN (электронное издание)1611-3349

    Конференция

    Конференция1st International Workshop on Connectomics in Artificial Intelligence in Radiation Therapy, AIRT 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
    Страна/TерриторияКитай
    ГородShenzhen
    Период17/10/1917/10/19

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