UI-Net: Interactive Artificial Neural Networks for Iterative Image Segmentation Based on a User Model

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dc.contributor.authorAmrehn, Marioen_US
dc.contributor.authorGaube, Svenen_US
dc.contributor.authorUnberath, Mathiasen_US
dc.contributor.authorSchebesch, Franken_US
dc.contributor.authorHorz, Timen_US
dc.contributor.authorStrumia, Maddalenaen_US
dc.contributor.authorSteidl, Stefanen_US
dc.contributor.authorKowarschik, Markusen_US
dc.contributor.authorMaier, Andreasen_US
dc.contributor.editorStefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Riederen_US
dc.date.accessioned2017-09-06T07:12:42Z
dc.date.available2017-09-06T07:12:42Z
dc.date.issued2017
dc.description.abstractFor complex segmentation tasks, fully automatic systems are inherently limited in their achievable accuracy for extracting relevant objects. Especially in cases where only few data sets need to be processed for a highly accurate result, semi-automatic segmentation techniques exhibit a clear benefit for the user. One area of application is medical image processing during an intervention for a single patient.We propose a learning-based cooperative segmentation approach which includes the computing entity as well as the user into the task. Our system builds upon a state-of-the-art fully convolutional artificial neural network (FCN) as well as an active user model for training. During the segmentation process, a user of the trained system can iteratively add additional hints in form of pictorial scribbles as seed points into the FCN system to achieve an interactive and precise segmentation result. The segmentation quality of interactive FCNs is evaluated. Iterative FCN approaches can yield superior results compared to networks without the user input channel component, due to a consistent improvement in segmentation quality after each interaction.en_US
dc.description.sectionheadersShort Papers
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20171248
dc.identifier.isbn978-3-03868-036-9
dc.identifier.issn2070-5786
dc.identifier.pages143-147
dc.identifier.urihttps://doi.org/10.2312/vcbm.20171248
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20171248
dc.publisherThe Eurographics Associationen_US
dc.subjectCCS Concepts
dc.subjectComputing methodologies
dc.subjectLearning from implicit feedback
dc.subjectNeural networks
dc.subjectSupervised learning by classification
dc.titleUI-Net: Interactive Artificial Neural Networks for Iterative Image Segmentation Based on a User Modelen_US
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VCBM 17: Eurographics Workshop on Visual Computing for Biology and Medicine