Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156702
Title: 3D multi-modality medical image registration with synthetic image augmentation using CycleGAN
Authors: Mukherjee, Mitali Nirmallya
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Mukherjee, M. N. (2022). 3D multi-modality medical image registration with synthetic image augmentation using CycleGAN. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156702
Abstract: This report proposes a 3D multi-modality medical image registration network with CycleGAN-based synthetic image augmentation. The method is designed for intra-subject brain CT-MRI registration. A broad overview of our method is to first generate a synthetic CT image from the MRI using the CycleGAN and then align it with the MRI using the registration network to learn a deformation field which is then used to register the MRI with the CT. Furthermore, we use image-to-image similarity metrics between the synthetic CT and the CT along with an additional auxiliary loss between the warped MRI and the CT. Finally, we perform thorough experiments on our method and prove that it outperforms the state-of-the-art methods and tools.
URI: https://hdl.handle.net/10356/156702
Fulltext Permission: embargo_restricted_20240430
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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