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

Abstract : 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).
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https://hal.telecom-paris.fr/hal-03325482
Contributor : Isabelle Bloch Connect in order to contact the contributor
Submitted on : Tuesday, August 24, 2021 - 10:01:16 PM
Last modification on : Tuesday, October 19, 2021 - 11:15:16 AM

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Vincent Couteaux, Mathilde Trintignac, Olivier Nempont, Guillaume Pizaine, Anna Sesilia Vlachomitrou, et al.. Cooperating Networks To Enforce A Similarity Constraint In Paired But Unregistered Multimodal Liver Segmentation. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Apr 2021, Nice, France. pp.753-756, ⟨10.1109/ISBI48211.2021.9433767⟩. ⟨hal-03325482⟩

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