Interactive Neural Network Robot User Investigation for Medical Image Segmentation
Interactive image segmentation bears the advantage of correctional updates to the current segmentation mask when compared to fully automated systems. Especially in the field of inter-operative medical image processing of a single patient, where a high accuracy is an uncompromisable necessity, a human operator guiding a system towards an optimal segmentation result is a time-efficient constellation benefiting the patient. There are recent categories of neural networks which can incorporate human-computer interaction (HCI) data as additional input for segmentation. In this work, we simulate this HCI data during training with state-of-the-art user models, also called robot users, which aim to act similar to real users given interactive image segmentation tasks. We analyze the influence of chosen robot users, which mimic different types of users and scribble patterns, on the segmentation quality. We conclude that networks trained with robot users with the most spread out seeding patterns generalize well during inference with other robot users.
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