Cooperating Networks To Enforce A Similarity Constraint In Paired But Unregistered Multimodal Liver Segmentation

We propose a method for segmenting two unregistered images from different modalities of the same patient and study at once, while enforcing a similarity constraint between the two segmentation masks. Our method relies on a segmentation network and a registration network, cooperating to get accurate...

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Bibliographic Details
Main Authors: Couteaux, Vincent, Trintignac, Mathilde, Nempont, Olivier, Pizaine, Guillaume, Vlachomitrou, Anna Sesilia, Valette, Pierre-Jean, Milot, Laurent, Bloch, Isabelle
Format: Conference Proceeding
Language:English
Subjects:
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Image segmentation
Learning systems
Liver
Magnetic resonance imaging
Multimodal Imaging
Registration
Segmentation
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Toy manufacturing industry
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Summary:We propose a method for segmenting two unregistered images from different modalities of the same patient and study at once, while enforcing a similarity constraint between the two segmentation masks. Our method relies on a segmentation network and a registration network, cooperating to get accurate and consistent segmentation masks across modalities, while forcing the segmentor to use all information available. Experiments on a dataset of T1 and T2-weighted liver MRI show that our method enables to get more similar segmentation masks across modalities than manual annotations, without deteriorating the performance (Dice =0.95 for T1, 0.92 for T2).
ISSN:1945-8452
DOI:10.1109/ISBI48211.2021.9433767