Alexandre Triay Bagur1,2, Ged Ridgway2, Sir Michael Brady2,3, and Daniel Bulte1
1Department of Engineering Science, The University of Oxford, Oxford, United Kingdom, 2Perspectum Ltd, Oxford, United Kingdom, 3Department of Oncology, The University of Oxford, Oxford, United Kingdom
1Department of Engineering Science, The University of Oxford, Oxford, United Kingdom, 2Perspectum Ltd, Oxford, United Kingdom, 3Department of Oncology, The University of Oxford, Oxford, United Kingdom
Automated pancreas parts segmentation using groupwise registration is feasible on healthy male volunteers of UK Biobank, with comparable performance to reference annotations. The method enables regional quantification of heterogeneous disease.
Figure 2. Variability in parts segmentation for the first 10 out of N=20 validation subjects for the first annotations (top), second annotations (middle), and our method’s predictions (bottom).
Figure 3. Illustration of the evaluation of parts segmentation using root mean squared error (RMSE) of the Euclidean distance between the annotation boundaries (black markers) and the predicted boundaries (solid colors). Illustrations for the intra-observer distance (left arrow), as well as distance between annotation 1 and prediction (right arrow), are shown.