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<https://discovery.ucl.ac.uk/id/eprint/10146183> <http://purl.org/dc/terms/title> "Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10146183> <http://purl.org/ontology/bibo/abstract> "The ability to adapt medical image segmentation networks for a novel class\r\nsuch as an unseen anatomical or pathological structure, when only a few\r\nlabelled examples of this class are available from local healthcare providers,\r\nis sought-after. This potentially addresses two widely recognised limitations\r\nin deploying modern deep learning models to clinical practice,\r\nexpertise-and-labour-intensive labelling and cross-institution generalisation.\r\nThis work presents the first 3D few-shot interclass segmentation network for\r\nmedical images, using a labelled multi-institution dataset from prostate cancer\r\npatients with eight regions of interest. We propose an image alignment module\r\nregistering the predicted segmentation of both query and support data, in a\r\nstandard prototypical learning algorithm, to a reference atlas space. The\r\nbuilt-in registration mechanism can effectively utilise the prior knowledge of\r\nconsistent anatomy between subjects, regardless whether they are from the same\r\ninstitution or not. Experimental results demonstrated that the proposed\r\nregistration-assisted prototypical learning significantly improved segmentation\r\naccuracy (p-values<0.01) on query data from a holdout institution, with varying\r\navailability of support data from multiple institutions. We also report the\r\nadditional benefits of the proposed 3D networks with 75% fewer parameters and\r\nan arguably simpler implementation, compared with existing 2D few-shot\r\napproaches that segment 2D slices of volumetric medical images."^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10146183> <http://purl.org/dc/terms/date> "2022-03-28" .
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<https://discovery.ucl.ac.uk/id/person/ext-883d73802d64ad4f5e4025349a831470> <http://xmlns.com/foaf/0.1/givenName> "Yiwen"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/person/ext-883d73802d64ad4f5e4025349a831470> <http://xmlns.com/foaf/0.1/familyName> "Li"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/person/ext-883d73802d64ad4f5e4025349a831470> <http://xmlns.com/foaf/0.1/name> "Yiwen Li"^^<http://www.w3.org/2001/XMLSchema#string> .
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<https://discovery.ucl.ac.uk/id/person/ext-4f378dc6db7ebbf6ceb829bd79c2c413> <http://xmlns.com/foaf/0.1/givenName> "Henkjan"^^<http://www.w3.org/2001/XMLSchema#string> .
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<https://discovery.ucl.ac.uk/id/person/ext-f617915a2df329c19c34f6688e2a5bf9> <http://xmlns.com/foaf/0.1/givenName> "Victor Adrian"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/person/ext-f617915a2df329c19c34f6688e2a5bf9> <http://xmlns.com/foaf/0.1/familyName> "Prisacariu"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/person/ext-f617915a2df329c19c34f6688e2a5bf9> <http://xmlns.com/foaf/0.1/name> "Victor Adrian Prisacariu"^^<http://www.w3.org/2001/XMLSchema#string> .
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<https://discovery.ucl.ac.uk/id/person/ext-f127eb4e82675e21a2dae0d941ca28f8> <http://xmlns.com/foaf/0.1/givenName> "Qianye"^^<http://www.w3.org/2001/XMLSchema#string> .
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<https://discovery.ucl.ac.uk/id/person/ext-a5b129616ed9a14e1bd72856efe5d0ed> <http://xmlns.com/foaf/0.1/givenName> "Yunguan"^^<http://www.w3.org/2001/XMLSchema#string> .
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