Brain extraction network trained with "silver standard" data and fine-tuned with manual annotation for improved segmentation
Training convolutional neural networks (CNNs) formedical image segmentation often requires large and representativesets of images and their corresponding annotations.Obtaining annotated images usually requires manual intervention,which is expensive and time consuming, as it typicallyrequires a specialist. An alternative approach is to leverageexisting automatic segmentation tools and combine them to createconsensus-based â€œsilver-standardsâ€ annotations. A drawback tothis approach is that silver-standards are usually smooth andthis smoothness is transmitted to the output segmentation ofthe network. Our proposal is to use a two-staged approach.First, silver-standard datasets are used to generate a large setof annotated images in order to train the brain extractionnetwork from scratch. Second, fine-tuning is performed usingmuch smaller amounts of manually annotated data so that thenetwork can learn the finer details that are not preserved inthe silver-standard data. As an example, our two-staged brainextraction approach has been shown to outperform seven state-of-the-art techniques across three different public datasets. Ourresults also suggest that CNNs can potentially capture inter-raterannotation variability between experts who annotate the sameset of images following the same guidelines, and also adapt todifferent annotation guidelines.
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