FEW-SHOT image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning
The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class are available from local healthcare providers, is sought-after. This potentially addresses two widely recognised limitati...
| Main Authors: | , , , , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
IEEE
2022
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| _version_ | 1826311841516617728 |
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| author | Li, Y Fu, Y Yang, Q Min, Z Yan, W Huisman, H Barratt, D Prisacariu, VA Hu, Y |
| author_facet | Li, Y Fu, Y Yang, Q Min, Z Yan, W Huisman, H Barratt, D Prisacariu, VA Hu, Y |
| author_sort | Li, Y |
| collection | OXFORD |
| description | The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class are available from local healthcare providers, is sought-after. This potentially addresses two widely recognised limitations in deploying modern deep learning models to clinical practice, expertise-and-labour-intensive labelling and cross-institution generalisation. This work presents the first 3D few-shot interclass segmentation network for medical images, using a labelled multi-institution dataset from prostate cancer patients with eight regions of interest. We propose an image alignment module registering the predicted segmentation of both query and support data, in a standard prototypical learning algorithm, to a reference atlas space. The built-in registration mechanism can effectively utilise the prior knowledge of consistent anatomy between subjects, regardless whether they are from the same institution or not. Experimental results demonstrated that the proposed registration-assisted prototypical learning significantly improved segmentation accuracy (p-values<0.01) on query data from a holdout institution, with varying availability of support data from multiple institutions. We also report the additional benefits of the proposed 3D networks with 75% fewer parameters and an arguably simpler implementation, compared with existing 2D few-shot approaches that segment 2D slices of volumetric medical images. |
| first_indexed | 2024-03-07T08:17:14Z |
| format | Conference item |
| id | oxford-uuid:72596c63-5217-4b37-8735-107336497b86 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T08:17:14Z |
| publishDate | 2022 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:72596c63-5217-4b37-8735-107336497b862024-01-11T14:18:02ZFEW-SHOT image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learningConference itemhttp://purl.org/coar/resource_type/c_5794uuid:72596c63-5217-4b37-8735-107336497b86EnglishSymplectic ElementsIEEE2022Li, YFu, YYang, QMin, ZYan, WHuisman, HBarratt, DPrisacariu, VAHu, YThe ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class are available from local healthcare providers, is sought-after. This potentially addresses two widely recognised limitations in deploying modern deep learning models to clinical practice, expertise-and-labour-intensive labelling and cross-institution generalisation. This work presents the first 3D few-shot interclass segmentation network for medical images, using a labelled multi-institution dataset from prostate cancer patients with eight regions of interest. We propose an image alignment module registering the predicted segmentation of both query and support data, in a standard prototypical learning algorithm, to a reference atlas space. The built-in registration mechanism can effectively utilise the prior knowledge of consistent anatomy between subjects, regardless whether they are from the same institution or not. Experimental results demonstrated that the proposed registration-assisted prototypical learning significantly improved segmentation accuracy (p-values<0.01) on query data from a holdout institution, with varying availability of support data from multiple institutions. We also report the additional benefits of the proposed 3D networks with 75% fewer parameters and an arguably simpler implementation, compared with existing 2D few-shot approaches that segment 2D slices of volumetric medical images. |
| spellingShingle | Li, Y Fu, Y Yang, Q Min, Z Yan, W Huisman, H Barratt, D Prisacariu, VA Hu, Y FEW-SHOT image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning |
| title | FEW-SHOT image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning |
| title_full | FEW-SHOT image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning |
| title_fullStr | FEW-SHOT image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning |
| title_full_unstemmed | FEW-SHOT image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning |
| title_short | FEW-SHOT image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning |
| title_sort | few shot image segmentation for cross institution male pelvic organs using registration assisted prototypical learning |
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