In medical imaging, segmentation ground truths generally suffer from large inter-observer variability. When multiple observers are used, simple fusion techniques are typically employed to combine multiple delineations into one consensus ground truth. However, in this process, potentially valuable information is discarded and it is yet unknown what strategy leads to optimal segmentation results. In this work, we compare several ground-truth types to train a U-net and apply it to the clinical use case of Barrett’s neoplasia detection. To this end, we have invited 14 international Barrett’s experts to delineate 2,851 neoplastic images derived from 812 patients into a higher- and lower-likelihood neoplasia areas. Five different ground-truths te...
Over the past decade, the imaging tools for endoscopists have improved drastically. This has enabled...
The surge of supervised learning methods for segmentation lately has underscored the critical role o...
The surge of supervised learning methods for segmentation lately has underscored the critical role o...
In medical imaging, segmentation ground truths generally suffer from large inter-observer variabilit...
In medical imaging, segmentation ground truths generally suffer from large inter-observer variabilit...
In medical imaging, segmentation ground truths generally suffer from large inter-observer variabilit...
In medical imaging, segmentation ground truths generally suffer from large inter-observer variabilit...
Deep learning (DL) methods have demonstrated superior performance in medical image segmentation task...
International challenges have become the standard for validation of biomedical image analysis method...
Uncertainty estimation methods are expected to improve the understanding and quality of computer-ass...
Over the past decade, the imaging tools for endoscopists have improved drastically. This has enabled...
Over the past decade, the imaging tools for endoscopists have improved drastically. This has enabled...
Over the past decade, the imaging tools for endoscopists have improved drastically. This has enabled...
Background Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-...
Over the past decade, the imaging tools for endoscopists have improved drastically. This has enabled...
Over the past decade, the imaging tools for endoscopists have improved drastically. This has enabled...
The surge of supervised learning methods for segmentation lately has underscored the critical role o...
The surge of supervised learning methods for segmentation lately has underscored the critical role o...
In medical imaging, segmentation ground truths generally suffer from large inter-observer variabilit...
In medical imaging, segmentation ground truths generally suffer from large inter-observer variabilit...
In medical imaging, segmentation ground truths generally suffer from large inter-observer variabilit...
In medical imaging, segmentation ground truths generally suffer from large inter-observer variabilit...
Deep learning (DL) methods have demonstrated superior performance in medical image segmentation task...
International challenges have become the standard for validation of biomedical image analysis method...
Uncertainty estimation methods are expected to improve the understanding and quality of computer-ass...
Over the past decade, the imaging tools for endoscopists have improved drastically. This has enabled...
Over the past decade, the imaging tools for endoscopists have improved drastically. This has enabled...
Over the past decade, the imaging tools for endoscopists have improved drastically. This has enabled...
Background Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-...
Over the past decade, the imaging tools for endoscopists have improved drastically. This has enabled...
Over the past decade, the imaging tools for endoscopists have improved drastically. This has enabled...
The surge of supervised learning methods for segmentation lately has underscored the critical role o...
The surge of supervised learning methods for segmentation lately has underscored the critical role o...