UI-Net: Interactive Artificial Neural Networks for Iterative Image Segmentation Based on a User Model
This work addresses the need for precise segmentation in scenarios like medical image processing during interventions, where fully automatic methods are limited, offering an incremental improvement over existing interactive techniques.
The paper tackles the problem of achieving high accuracy in complex image segmentation tasks by proposing a semi-automatic, interactive approach that combines a fully convolutional neural network with user-provided scribbles, resulting in consistent improvements in segmentation quality after each user interaction.
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.