1 citations · 2 across the 2 of their papers we have counts for
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Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study
Robert Robinson, Vanya V. Valindria, Wenjia Bai +19
Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic proc…
Small Organ Segmentation in Whole-body MRI using a Two-stage FCN and Weighting Schemes
Vanya V. Valindria, Ioannis Lavdas, Juan Cerrolaza +4
Accurate and robust segmentation of small organs in whole-body MRI is difficult due to anatomical variation and class imbalance. Recent deep network based approaches have demonstra…
Real-time Prediction of Segmentation Quality
Robert Robinson, Ozan Oktay, Wenjia Bai +17
Recent advances in deep learning based image segmentation methods have enabled real-time performance with human-level accuracy. However, occasionally even the best method fails due…
Domain Adaptation for MRI Organ Segmentation using Reverse Classification Accuracy
Vanya V. Valindria, Ioannis Lavdas, Wenjia Bai +5
The variations in multi-center data in medical imaging studies have brought the necessity of domain adaptation. Despite the advancement of machine learning in automatic segmentatio…
Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth
Vanya V. Valindria, Ioannis Lavdas, Wenjia Bai +5
When integrating computational tools such as automatic segmentation into clinical practice, it is of utmost importance to be able to assess the level of accuracy on new data, and i…