Cooperating Networks To Enforce A Similarity Constraint In Paired But Unregistered Multimodal Liver Segmentation - Archive ouverte HAL Access content directly
Conference Papers Year : 2021

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

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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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Dates and versions

hal-03325482 , version 1 (24-08-2021)

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Cite

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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