T
Tom Doel
Researcher at University College London
Publications - 24
Citations - 2227
Tom Doel is an academic researcher from University College London. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 12, co-authored 23 publications receiving 1521 citations. Previous affiliations of Tom Doel include Toshiba & University College London Hospitals NHS Foundation Trust.
Papers
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Journal ArticleDOI
Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning
Guotai Wang,Wenqi Li,Maria A. Zuluaga,Rosalind Pratt,Premal A. Patel,Michael Aertsen,Tom Doel,Anna L. David,Jan Deprest,Sebastien Ourselin,Tom Vercauteren +10 more
TL;DR: A novel deep learning-based interactive segmentation framework by incorporating CNNs into a bounding box and scribble-based segmentation pipeline and proposing a weighted loss function considering network and interaction-based uncertainty for the fine tuning is proposed.
Journal ArticleDOI
NiftyNet: a deep-learning platform for medical imaging
Eli Gibson,Wenqi Li,Carole H. Sudre,Lucas Fidon,Dzhoshkun I. Shakir,Guotai Wang,Zach Eaton-Rosen,Robert Gray,Tom Doel,Yipeng Hu,Tom Whyntie,Parashkev Nachev,Marc Modat,Dean C. Barratt,Sebastien Ourselin,M. Jorge Cardoso,Tom Vercauteren +16 more
TL;DR: An open-source platform is implemented based on TensorFlow APIs for deep learning in medical imaging domain that facilitates warm starts with established pre-trained networks, adapting existing neural network architectures to new problems, and rapid prototyping of new solutions.
Journal ArticleDOI
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Guotai Wang,Maria A. Zuluaga,Wenqi Li,Rosalind Pratt,Premal A. Patel,Michael Aertsen,Tom Doel,Anna L. David,Jan Deprest,Sebastien Ourselin,Tom Vercauteren +10 more
TL;DR: A deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy, and obtains comparable and even higher accuracy with fewer user interventions and less time compared with traditional interactive methods.
Journal ArticleDOI
Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning
Guotai Wang,Wenqi Li,Maria A. Zuluaga,Rosalind Pratt,Premal A. Patel,Michael Aertsen,Tom Doel,Anna L. David,Jan Deprest,Sebastien Ourselin,Tom Vercauteren +10 more
TL;DR: In this article, the authors proposed a novel deep learning-based framework for interactive segmentation by incorporating CNNs into a bounding box and scribble-based segmentation pipeline.
Journal ArticleDOI
An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI.
Michael Ebner,Michael Ebner,Guotai Wang,Guotai Wang,Guotai Wang,Wenqi Li,Wenqi Li,Michael Aertsen,Premal A. Patel,Premal A. Patel,Rosalind Aughwane,Andrew Melbourne,Andrew Melbourne,Tom Doel,Steven Dymarkowski,Paolo De Coppi,Anna L. David,Anna L. David,Jan Deprest,Jan Deprest,Sebastien Ourselin,Tom Vercauteren,Tom Vercauteren,Tom Vercauteren +23 more
TL;DR: A fully automatic framework for fetal brain reconstruction that consists of four stages that outperforms state-of-the-art methods in both segmentation and reconstruction comparisons including expert-reader quality assessments is proposed.