TY  - INPR
UR  - http://dx.doi.org/10.1109/ISBI52829.2022.9761453
A1  - Li, Yiwen
A1  - Fu, Yunguan
A1  - Yang, Qianye
A1  - Min, Zhe
A1  - Yan, Wen
A1  - Huisman, Henkjan
A1  - Prisacariu, Victor Adrian
A1  - Barratt, Dean
A1  - Hu, Yipeng
N2  - 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.
AV  - public
KW  - eess.IV
KW  -  eess.IV
KW  -  cs.CV
ID  - discovery10146183
CY  - Kolkata, India
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
PB  - IEEE
TI  - Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning
Y1  - 2022/03/28/
ER  -