Interactive segmentation using U-Net with weight map and dynamic user interactions
This work addresses segmentation accuracy in medical imaging for experts, but it is incremental as it builds on existing U-Net methods with modifications.
The paper tackles interactive segmentation for medical images by proposing a framework that adapts user click sizes based on the current mask and uses a weighted loss function, resulting in accuracy improvements of 5.60% and 10.39% with a single interaction on spleen and colon cancer CT images.
Interactive segmentation has recently attracted attention for specialized tasks where expert input is required to further enhance the segmentation performance. In this work, we propose a novel interactive segmentation framework, where user clicks are dynamically adapted in size based on the current segmentation mask. The clicked regions form a weight map and are fed to a deep neural network as a novel weighted loss function. To evaluate our loss function, an interactive U-Net (IU-Net) model which applies both foreground and background user clicks as the main method of interaction is employed. We train and validate on the BCV dataset, while testing on spleen and colon cancer CT images from the MSD dataset to improve the overall segmentation accuracy in comparison to the standard U-Net using our weighted loss function. Applying dynamic user click sizes increases the overall accuracy by 5.60% and 10.39% respectively by utilizing only a single user interaction.