Abstract
Intracranial hemorrhage (ICH) is a dangerous condition of bleeding within the skull that calls for rapid and precise diagnosis due to potentially fatal consequences. In this paper, we propose Residual Segmentation with Generative Adversarial Networks (ReSGAN) to accurately localize the hemorrhage from computerized tomography (CT) scans with a GAN-based model. Although convolutional neural networks have shown success in the ICH segmentation task, precise localization remains challenging due to in-balance and scarcity of labeled training data. Synthetic samples from generative models, and aligned templates as reference from brain atlas have been demonstrated to alleviate the issues. We consider synthetic templates as another candidate and solve the problem by directly applying a generative model to segmentation. Our ReSGAN learns a distribution of pseudo-normal brain CT scans, that through residuals, reliably delineates the hemorrhaging areas. We perform experiments on two datasets and compare our model against a well established baseline, that consistently shows significant improvements, therefore demonstrating the validity of our novel method.
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Acknowledgment
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (NRF-No. 2021R1A2C3011169)[30%], Electronics and Telecommunications Research Institute(ETRI) grant funded by the Korean government[21ZS1100, Core Technology Research for Self-Improving Integrated Artificial Intelligence System][30%], and the Industrial Strategic Technology Development Program(20011875, Development of AI based diagnostic technology for medical imaging devices) funded By the Ministry of Trade, Industry & Energy(MOTIE, Korea)[40%]
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Toikkanen, M., Kwon, D., Lee, M. (2021). ReSGAN: Intracranial Hemorrhage Segmentation with Residuals of Synthetic Brain CT Scans. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12901. Springer, Cham. https://doi.org/10.1007/978-3-030-87193-2_38
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