2019/10/17 by Kuan-Lun Tseng, Tseng, Kuan-Lun, Winston H. Hsu +9
Computer Science · Engineering · Medicine · Neuroscience · #Advanced Neural Network Applications #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Radiomics and Machine Learning in Medical Imaging #cs.CV #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1910.07800
arxiv created 2019/10/17 · openalex publication_date 2019/10/17 · arxiv updated 2019/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the development of image segmentation in computer vision, biomedical image segmentation have achieved remarkable progress on brain tumor segmentation and Organ At Risk (OAR) segmentation. However, most of the research only uses single modality such as Computed Tomography (CT) scans while in real world scenario doctors often use multiple modalities to get more accurate result. To better leverage different modalities, we have collected a large dataset consists of 136 cases with CT and MR images which diagnosed with nasopharyngeal cancer. In this paper, we propose to use Generative Adversarial Network to perform CT to MR transformation to synthesize MR images instead of aligning two modalities. The synthesized MR can be jointly trained with CT to achieve better performance. In addition, we use instance segmentation model to extend the OAR segmentation task to segment both organs and tumor region. The collected dataset will be made public soon.