# Interactive segmentation using U-Net with weight map and dynamic user interactions

arXiv: https://arxiv.org/abs/2111.09740 · HTML: https://mlsanity.com/paper/2111.09740/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 8 references that are in the map.
- First posted on arXiv 18 Nov 2021.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation](https://mlsanity.com/paper/1810.07842/) (2018)
- [Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning](https://mlsanity.com/paper/1710.04043/) (2017)
- [DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation](https://mlsanity.com/paper/1707.00652/) (2017)
- [Interactive segmentation of medical images through fully convolutional neural networks](https://mlsanity.com/paper/1903.08205/) (2019)
- [Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells](https://mlsanity.com/paper/1802.07465/) (2018)

## Built on by (most cited first, 2 of 2)

- [Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2311.13964/) (2023)
- [Guiding the Guidance: A Comparative Analysis of User Guidance Signals for Interactive Segmentation of Volumetric Images](https://mlsanity.com/paper/2303.06942/) (2023)

## Abstract (arXiv)

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.