@inproceedings{78cdb3691312473d922beb05baf38cb3,
title = "Anatomy-Guided Synthesis of Novel CT Images at Full Hounsfield Range",
abstract = "In this work, we present an approach that can synthesize novel CT images across the full Hounsfield range using a very small annotated dataset of around thirty patients and a large non-annotated dataset with high resolution medical images. Our method uses these two datasets in a sequence of steps involving texture learning via StyleGAN and semi-supervised learning via CycleGAN to generate a large annotated medical dataset suitable for use in deep learning algorithms for medical applications. Using an anatomy exploration interface we can then generate CT images with anatomies that were non-existent within either of the datasets, without compromising accuracy and quality. We show that our approach works for all Hounsfield windows with minimal depreciation in anatomical plausibility.",
keywords = "deep learning, GAN, medical imaging, style loss",
author = "Arjun Krishna and Ge Wang and Klaus Mueller",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022 ; Conference date: 05-11-2022 Through 12-11-2022",
year = "2022",
doi = "10.1109/NSS/MIC44845.2022.10399212",
language = "English",
series = "2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference",
}