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

By Mario P Amrehn, Sven Gaube, Mathias Unberath, Frank Schebesch, Tim Horz, Maddalena Strumia, Stefan Steidl, M. Kowarschik, Andreas K. Maier. 2017

37 citations recorded by the source corpus.

For 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Human-Computer Interaction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Emma Claire Robinson
2. Yuta Nakashima
3. Bernhard Kainz
4. Samuel Budd
5. Liangzhi Li

In Computer Vision and Pattern Recognition

1. Emma Claire Robinson
2. Yuta Nakashima
3. Bernhard Kainz
4. Samuel Budd
5. Liangzhi Li

Citations in the corpus

• Iterative Interaction Training for Segmentation Editing Networks (2018, neutral): “In recent years, studies such as [1,9,19] proposed CNNs for interactive segmentations and showed better results compared to traditional methods.”
• A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis (2019, background): “Amrehn et al. (2017) introduced UI-Net, that builds on the popular U-Net architecture for medical image segmentation Ronneberger et al. (2015).”
• IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks (2019, methodology): “For example, the iterated interactive model [12] runs the FCN model several times and takes the users’ feedback to add more accurate training labels during each iteration.”