Robust Segmentation

The delineation of regions in medical images - segmentation - is a key task across modalities and organs. Methods developed need to produce segmentations that are biologically meaningful across scanners and imaging protocols.

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Papers

	UniFed: A unified deep learning framework for segmentation of partially labelled, distributed neuroimaging data Nicola K Dinsdale, Mark Jenkinson, Ana IL Namburete bioRxiv, 2024 Project Page / Paper / Code We, therefore, propose UniFed, a unified federated harmoni- sation framework, which enables three key processes to be completed: 1) the training of a federated partially labelled harmonisation network, 2) the selection of the most appropriate pretrained model for a new unseen site, and 3) the incorporation of a new site into the harmonised federation.
	STAMP: Simultaneous Training and Model Pruning for Low Data Regimes in Medical Image Segmentation Nicola K Dinsdale , Mark Jenkinson, Ana IL Namburete Medical Image Analysis, 2022 Project Page / Paper / Code Development of an algorithm to enable simultaneous training and pruning, enabling segmentation in low data domains. Also introduces Targeted Dropout to stabilise the pruning.
	Omni-Supervised Domain Adversarial Training for White Matter Hyperintensity Segmentation in the UK Biobank Vaanathi Sundaresan, Nicola K Dinsdale, Ludovica Griffanti, Mark Jenkinson, ISBI 2022 [Oral Presentation] Project Page / Paper / Code Exploring the use of omni-supervised learning for white matter hyperintensity segmentation, using age prediction as the selection criteria. Leads to significant increase in sensitivity especially for small lesions.
	The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects Erica Balboni, Luca Nocetti, Chiara Carbone, Nicola K Dinsdale , Maurilio Genovese, Gabriele Guidi, Marcella Malagoli, Annalisa Chiari, Ana IL Namburete, Mark Jenkinson, Giovanna Zamboni, Human Brain Mapping 2022 Paper / Code Exploration of the use of my SWANS algorithm for clinical data, across disease groups - SWANS first presented at MICCAI 2019
	TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations Madeleine K Wyburd, Nicola K Dinsdale , Ana IL Namburete, Mark Jenkinson MICCAI, 2021 [Early Acceptance] Project Page / Paper / Code Novel segmentation method guaranteeing accurate topology.
	Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images Vaanathi Sundaresan, Giovanna Zamboni, Nicola K Dinsdale , Peter Rothwell, Ludovica Griffanti, Mark Jenkinson Medical Image Analysis 2021 Paper / Code Comparison of DA methods for white matter hyperintensity segmentation, comparing methods including my proposed method: paper.
	A 2-step deep learning method with domain adaptation for multi-centre, multi-vendor and multi-disease cardiac magnetic resonance segmentation Jorge Corral Acero, Vaanathi Sundaresan, Nicola K Dinsdale , Vicente Grau, Mark Jenkinson STACOM 2020 - 6th place Paper Segmentation of cardiac MRI across sites, using the method proposed in my paper
	Spatial warping network for 3D segmentation of the hippocampus in MR images Nicola K Dinsdale , Mark Jenkinson, Ana IL Namburete MICCAI, 2019 [Early Acceptance] Paper / Code Novel segmentation method based on a spatial transformer network.

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