Extended Modality Propagation: Image Synthesis of Pathological Cases

• Published in: IEEE transactions on medical imaging Vol. 35; no. 12; pp. 2598 - 2608
• Main Authors: Cordier, Nicolas, Delingette, Herve, Le, Matthieu, Ayache, Nicholas
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Brain; Brain - diagnostic imaging; Brain cancer; Brain modeling; Brain Neoplasms - diagnostic imaging; Brain tumors; Computer Science; Datasets; Generative model; Glioma; Growth models; Humans; Image generation; Image Interpretation, Computer-Assisted - methods; Image Processing; Image segmentation; Iterative methods; Magnetic Resonance Imaging; medical image simulation; Medical imaging; modality synthesis; multi-atlas; Multimodal Imaging - methods; Neuroimaging; patch-based; Pathology; Propagation; Synthesis; Training; Tumors; glioma; multi-atlas; patch-based; generative model; medical image simulation; modality synthesis
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2016.2589760

This paper describes a novel generative model for the synthesis of multi-modal medical images of pathological cases based on a single label map. Our model builds upon i) a generative model commonly used for label fusion and multi-atlas patch-based segmentation of healthy anatomical structures, ii) the Modality Propagation iterative strategy used for a spatially-coherent synthesis of subject-specific scans of desired image modalities. The expression Extended Modality Propagation is coined to refer to the extension of Modality Propagation to the synthesis of images of pathological cases. Moreover, image synthesis uncertainty is estimated. An application to Magnetic Resonance Imaging synthesis of glioma-bearing brains is i) validated on the training dataset of a Multimodal Brain Tumor Image Segmentation challenge, ii) compared to the state-of-the-art in glioma image synthesis, and iii) illustrated using the output of two different tumor growth models. Such a generative model allows the generation of a large dataset of synthetic cases, which could prove useful for the training, validation, or benchmarking of image processing algorithms.