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Manzanera, O.E.M.* ; Ellis, S.* ; Baltatzis, V.* ; Nair, A.* ; Le Folgoc, L.* ; Desai, S.* ; Glocker, B.* ; Schnabel, J.A.*

Patient-specific 3d cellular automata nodule growth synthesis in lung cancer without the need of external data.

In: (2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 13-16 April 2021, Nice, France). 2021. 925-928 (Proceedings - International Symposium on Biomedical Imaging ; 2021-April)
DOI
We propose a novel patient-specific generative approach to simulate the emergence and growth of lung nodules using 3D cellular automata (CA) in computer tomography (CT). Our proposed method can be applied to individual images thus eliminating the need of external images that can contaminate and influence the generative process, a valuable characteristic in the medical domain. Firstly, we employ inpainting to generate pseudo-healthy representations of lung CT scans prior the visible appearance of each lung nodule. Then, for each nodule, we train a 3D CA to simulate nodule growth and progression using the image of that same nodule as a target. After each CA is trained, we generate early versions of each nodule from a single voxel until the growing nodule closely matches the appearance of the original nodule. These synthesized nodules are inserted where the original nodule was located in the pseudo-healthy inpainted versions of the CTs, which provide realistic context to the generated nodule. We utilize the simulated images for data augmentation yielding false positive reduction in a nodule detector. We found statistically significant improvements (p lt 0.001) in the detection of lung nodules.
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Publication type Article: Conference contribution
Corresponding Author
Keywords Deep Learning ; Generative Models
ISSN (print) / ISBN 1945-7928
e-ISSN 1945-8452
Conference Title 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Conference Date 13-16 April 2021
Conference Location Nice, France
Quellenangaben Volume: 2021-April, Issue: , Pages: 925-928 Article Number: , Supplement: ,
Non-patent literature Publications
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)