Ben Glocker

TL;DR: By combining FCN with a large-scale annotated dataset, this work shows for the first time that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinical measures.

TL;DR: The work of the BioMedIA group in this area is outlined and some of the challenges and opportunities for future work are highlighted, including data-driven, semantic and intelligent medical imaging.

TL;DR: In this article, the authors explored clinically acceptable autocontouring solutions that can be integrated into existing workflows and used in different domains of radiotherapy and evaluated on both internal and external data sets.

TL;DR: Three state‐of‐the‐art algorithms were developed and used to automatically segment major organs and bones in whole body MRI; good agreement to manual segmentations performed by clinical MRI experts was observed.

TL;DR: Morpho-MNIST as mentioned in this paper is a framework that aims to answer "to what extent has my model learned to represent specific factors of variation in the data?" They extend the MNIST dataset by adding a morphometric analysis enabling quantitative comparison of trained models, identification of the roles of latent variables, and characterisation of sample diversity.