Interactive Image Segmentation via Backpropagating Refinement Scheme
Abstract
An interactive image segmentation algorithm, which accepts user-annotations about a target object and the background, is proposed in this work. We convert user-annotations into interaction maps by measuring distances of each pixel to the annotated locations. Then, we perform the forward pass in a convolutional neural network, which outputs an initial segmentation map. However, the user-annotated locations can be mislabeled in the initial result. Therefore, we develop the backpropagating refinement scheme (BRS), which corrects the mislabeled pixels. Experimental results demonstrate that the proposed algorithm outperforms the conventional algorithms on four challenging datasets. Furthermore, we demonstrate the generality and applicability of BRS in other computer vision tasks, by transforming existing convolutional neural networks into user-interactive ones.
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Preliminary experiments showed no meaningful gain on using mappings other than the binary matrix for encoding user interactions.
“Although a common approach is to process these inputs with 2D Gaussian kernels of different spreads [27], preliminary experiments showed no meaningful gain on using mappings other than the binary matrix.”
From Interactive Deep Learning-Based Retinal OCT Layer Segmentation Refinement by Regressing Translation Maps (2024) · cites this paper as [27]
Cites
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A survey on active learning and human-in-the-loop deep learning for medical image analysis
Budd, S., Robinson, E. C., & Kainz, B. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102062 · Cited by 606 · see citing works
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A deep learning-based interactive medical image segmentation framework with sequential memory
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PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts
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Li, G., Zhang, G., & Qin, C. (2021) · Sensors · doi:10.3390/s21186100 · Cited by 2 · see citing works
“Sofiiuk, K.; Petrov, I.; Barinova, O.; Konushin, A. F-BRS: Rethinking Backpropagating Refinement for Interactive Segmenta- tion.”
Method
- Authors
- Jang, W.-D., & Kim, C.-S.
- Affiliations
- Harvard University (US)
- Korea University (KR)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Volume
- 2019
- Pages
- 5292-5301
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2948553897
- Cited by
- 234
- References
- 6
- Citations by year
- 2026: 11 · 2025: 28 · 2024: 50 · 2023: 43 · 2022: 44 · 2021: 35 · 2020: 21 · 2019: 2
- FWCI
- 8.31
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- IEEE Conference Proceedings
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- interactive segmentation, user annotation, interaction maps, convolutional neural networks
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