Flexible Conditional Image Generation of Missing Data with Learned Mental Maps
Hou B, Vlontzos A, Alansary A, Rueckert D, Kainz B (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer
Book Volume: 11905 LNCS
Pages Range: 139-150
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Shenzhen, CHN
ISBN: 9783030338428
DOI: 10.1007/978-3-030-33843-5_13
Abstract
Real-world settings often do not allow acquisition of high-resolution volumetric images for accurate morphological assessment and diagnostic. In clinical practice it is frequently common to acquire only sparse data (e.g. individual slices) for initial diagnostic decision making. Thereby, physicians rely on their prior knowledge (or mental maps) of the human anatomy to extrapolate the underlying 3D information. Accurate mental maps require years of anatomy training, which in the first instance relies on normative learning, i.e. excluding pathology. In this paper, we leverage Bayesian Deep Learning and environment mapping to generate full volumetric anatomy representations from none to a small, sparse set of slices. We evaluate proof of concept implementations based on Generative Query Networks (GQN) and Conditional BRUNO using abdominal CT and brain MRI as well as in a clinical application involving sparse, motion-corrupted MR acquisition for fetal imaging. Our approach allows to reconstruct 3D volumes from 1 to 4 tomographic slices, with a SSIM of 0.7+ and cross-correlation of 0.8+ compared to the 3D ground truth.
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United Kingdom (GB)How to cite
APA:
Hou, B., Vlontzos, A., Alansary, A., Rueckert, D., & Kainz, B. (2019). Flexible Conditional Image Generation of Missing Data with Learned Mental Maps. In Florian Knoll, Andreas Maier, Daniel Rueckert, Jong Chul Ye (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 139-150). Shenzhen, CHN: Springer.
MLA:
Hou, Benjamin, et al. "Flexible Conditional Image Generation of Missing Data with Learned Mental Maps." Proceedings of the 2nd International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2019 held in Conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Florian Knoll, Andreas Maier, Daniel Rueckert, Jong Chul Ye, Springer, 2019. 139-150.
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