Interactive Scribble Segmentation

Mathias Micheelsen Lowes, Jakob Lønborg Christensen, Bjørn Schreblowski Hansen, Morten Rieger Hannemose, Anders Bjorholm Dahl, Vedrana Andersen Dahl

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We present a deep learning model for image segmentation that uses weakly supervised inputs consisting of scribbles. A user can draw scribbles on an image with a brush tool corresponding to the labels they want segmented. The network can segment images in real time while scribbles are being drawn, giving instant feedback to the user. It is easy to correct mistakes made by the network, as more scribbles can be added. During training we use a similar psuedo-interactive and iterative setup to make sure that the network is optimized towards the human-in-the-loop inference setting. On the contrary, standard scribble segmentation methods do not consider the training of the algorithm as an interactive setting and thus are not suited for interactive inference. Our model is class-agnostic and we are able to generalize across many different data modalities. We compare our model with other weakly supervised methods such as bounding box and extrema point methods, and we show our model achieves a better mean DICE score.
Original languageEnglish
Title of host publicationProceedings of the Northern Lights Deep Learning Workshop 2023
Number of pages8
PublisherSeptentrio Academic Publishing
Publication date2023
Publication statusPublished - 2023
Event Northern Lights Deep Learning Workshop 2023 - Tromsø, Norway
Duration: 10 Jan 202312 Jan 2023


Workshop Northern Lights Deep Learning Workshop 2023


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