Synthesis of Annotated Data for Medical Image Segmentation: (a Chapter of "Generative Machine Learning Models in Medical Image Computing")

TY - CHAP

T1 - Synthesis of Annotated Data for Medical Image Segmentation

T2 - (a Chapter of "Generative Machine Learning Models in Medical Image Computing")

AU - Fernandez, Virginia

AU - Borges, Pedro

AU - Graham, Mark

AU - Lopez Pinaya, Walter Hugo

AU - Vercauteren, Tom

AU - Cardoso, Jorge

PY - 2025

Y1 - 2025

N2 - the past decade, the advances in deep learning technologies have enabledtheir application to medical image segmentation, showing great potential.Nonetheless, the scarcity of available labelled data can result in a lack of model generalisability. This is especially true for supervised methods requiring annotated data. Data augmentation can partially alleviate data scarcity when training deep learning models. In particular, deep learning-based generative modelling, which allows for sampling synthetic data from the modelled data distribution, has shown its potential for data augmentation in the past years. In this work, we address the topic of generative modelling to generate images and annotations, going over brainSPADE, a 2D and 3D generative model of healthy and pathological segmentations and corresponding multi-modal images for brain MRI, and how the synthetic data it produces can be applied to a range of segmentation tasks to mitigate the effects of data scarcity or domain shift.

AB - the past decade, the advances in deep learning technologies have enabledtheir application to medical image segmentation, showing great potential.Nonetheless, the scarcity of available labelled data can result in a lack of model generalisability. This is especially true for supervised methods requiring annotated data. Data augmentation can partially alleviate data scarcity when training deep learning models. In particular, deep learning-based generative modelling, which allows for sampling synthetic data from the modelled data distribution, has shown its potential for data augmentation in the past years. In this work, we address the topic of generative modelling to generate images and annotations, going over brainSPADE, a 2D and 3D generative model of healthy and pathological segmentations and corresponding multi-modal images for brain MRI, and how the synthetic data it produces can be applied to a range of segmentation tasks to mitigate the effects of data scarcity or domain shift.

UR - https://link.springer.com/book/10.1007/978-3-031-80965-1?page=2#toc

U2 - 10.1007/978-3-031-80965-1

DO - 10.1007/978-3-031-80965-1

M3 - Chapter

SN - 978-3-031-80964-4

SP - 3

EP - 24

BT - Generative Machine Learning Models in Medical Image Computing

PB - Springer

ER -