Multilabel DeepEdit
Multilabel DeepEdit generalizes the DeepEdit App. This means it addresses the single and multilabel segmentation tasks. Similar to DeepEdit, this App combines the power of two models in one single architecture: automatic inference, as a standard segmentation method (i.e. UNet), and interactive segmentation using clicks.
Similar to the single label DeepEdit, the training process of the multilabel DeepEdit App involves a combination of simulated clicks and standard training. As shown in the next figure, the input of the network is a concatenation of N tensors: image, and one channel per label including background containing the simulated points or clicks. This model has two types of training: For some iterations, tensors representing the clicks for each label are zeros and for the other part of the iterations, clicks are simulated so the model can receive inputs for interactive segmentation. For the clicks simulation, we developed new DeepGrow transforms that support multilabel click simulation.
