TY - GEN
T1 - Demystifying T1-MRI to FDG 18 -PET Image Translation via Representational Similarity
AU - Kao, Chia Hsiang
AU - Chen, Yong Sheng
AU - Chen, Li Fen
AU - Chiu, Wei Chen
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021/9
Y1 - 2021/9
N2 - Recent development of image-to-image translation techniques has enabled the generation of rare medical images (e.g., PET) from common ones (e.g., MRI). Beyond the potential benefits of the reduction in scanning time, acquisition cost, and radiation exposure risks, the translation models in themselves are inscrutable black boxes. In this work, we propose two approaches to demystify the image translation process, where we particularly focus on the T1-MRI to PET translation. First, we adopt the representational similarity analysis and discover that the process of T1-MR to PET image translation includes the stages of brain tissue segmentation and brain region recognition, which unravels the relationship between the structural and functional neuroimaging data. Second, based on our findings, an Explainable and Simplified Image Translation (ESIT) model is proposed to demonstrate the capability of deep learning models for extracting gray matter volume information and identifying brain regions related to normal aging and Alzheimer’s disease, which untangles the biological plausibility hidden in deep learning models.
AB - Recent development of image-to-image translation techniques has enabled the generation of rare medical images (e.g., PET) from common ones (e.g., MRI). Beyond the potential benefits of the reduction in scanning time, acquisition cost, and radiation exposure risks, the translation models in themselves are inscrutable black boxes. In this work, we propose two approaches to demystify the image translation process, where we particularly focus on the T1-MRI to PET translation. First, we adopt the representational similarity analysis and discover that the process of T1-MR to PET image translation includes the stages of brain tissue segmentation and brain region recognition, which unravels the relationship between the structural and functional neuroimaging data. Second, based on our findings, an Explainable and Simplified Image Translation (ESIT) model is proposed to demonstrate the capability of deep learning models for extracting gray matter volume information and identifying brain regions related to normal aging and Alzheimer’s disease, which untangles the biological plausibility hidden in deep learning models.
KW - Explainability
KW - Medical image translation
UR - http://www.scopus.com/inward/record.url?scp=85116475694&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-87199-4_38
DO - 10.1007/978-3-030-87199-4_38
M3 - Conference contribution
AN - SCOPUS:85116475694
SN - 9783030871987
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 402
EP - 412
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
A2 - de Bruijne, Marleen
A2 - Cattin, Philippe C.
A2 - Cotin, Stéphane
A2 - Padoy, Nicolas
A2 - Speidel, Stefanie
A2 - Zheng, Yefeng
A2 - Essert, Caroline
PB - Springer Science and Business Media Deutschland GmbH
T2 - 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
Y2 - 27 September 2021 through 1 October 2021
ER -