Iterative Interaction Training for Segmentation Editing Networks
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Date
2018-09-15
Publication Type
Conference Paper
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yes
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Abstract
Automatic segmentation has great potential to facilitate morphological measurements while simultaneously increasing efficiency. Nevertheless often users want to edit the segmentation to their own needs and will need different tools for this. There has been methods developed to edit segmentations of automatic methods based on the user input, primarily for binary segmentations. Here however, we present an unique training strategy for convolutional neural networks (CNNs) trained on top of an automatic method to enable interactive segmentation editing that is not limited to binary segmentation. By utilizing a robot-user during training, we closely mimic realistic use cases to achieve optimal editing performance. In addition, we show that an increase of the iterative interactions during the training process up to ten improves the segmentation editing performance substantially. Furthermore, we compare our segmentation editing CNN (interCNN) to state-of-the-art interactive segmentation algorithms and show a superior or on par performance.
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published
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Book title
Machine Learning in Medical Imaging
Journal / series
Lecture Notes in Computer Science
Volume
11046
Pages / Article No.
363 - 370
Publisher
Springer
Event
Machine Learning in Medical Imaging
Edition / version
Methods
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Organisational unit
09579 - Konukoglu, Ender / Konukoglu, Ender
02652 - Institut für Bildverarbeitung / Computer Vision Laboratory