mlsanityData to 14 Oct 2026

Scribble2D5: Weakly-Supervised Volumetric Image Segmentation via Scribble Annotations

arXiv 2205.06779 ↗First posted 13 May 2022cs.CVcited by 15

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Abstract (arXiv)

Recently, weakly-supervised image segmentation using weak annotations like scribbles has gained great attention, since such annotations are much easier to obtain compared to time-consuming and label-intensive labeling at the pixel/voxel level. However, because scribbles lack structure information of region of interest (ROI), existing scribble-based methods suffer from poor boundary localization. Furthermore, most current methods are designed for 2D image segmentation, which do not fully leverage the volumetric information if directly applied to image slices. In this paper, we propose a scribble-based volumetric image segmentation, Scribble2D5, which tackles 3D anisotropic image segmentation and improves boundary prediction. To achieve this, we augment a 2.5D attention UNet with a proposed label propagation module to extend semantic information from scribbles and a combination of static and active boundary prediction to learn ROI's boundary and regularize its shape. Extensive experiments on three public datasets demonstrate Scribble2D5 significantly outperforms current scribble-based methods and approaches the performance of fully-supervised ones. Our code is available online.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 19 of its 30 references that are in the map. First posted on arXiv 13 May 2022.

Builds on

19 of 30 references.

References · 19

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  3. 2015Holistically-Nested Edge Detectioncited by 592
  4. 2016ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentationcited by 331
  5. 2018Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentationcited by 321
  6. 2020CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentationcited by 309
  7. 2017Deep Extreme Cut: From Extreme Points to Object Segmentationcited by 127
  8. 2018Normalized Cut Loss for Weakly-supervised CNN Segmentationcited by 112
  9. 2020Weakly-Supervised Salient Object Detection via Scribble Annotationscited by 96
  10. 2016DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networkscited by 79
  11. 2022CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervisioncited by 64
  12. 2022Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervisioncited by 64
7 more
  1. 2018Constrained-CNN losses for weakly supervised segmentationcited by 62
  2. 2017W-Net: A Deep Model for Fully Unsupervised Image Segmentationcited by 57
  3. 2020Going to Extremes: Weakly Supervised Medical Image Segmentationcited by 38
  4. 2020Scribble-based Domain Adaptation via Co-segmentationcited by 34
  5. 2021Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentationcited by 21
  6. 2021ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentationcited by 15
  7. 2017Employing Weak Annotations for Medical Image Analysis Problemscited by 7

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Citing papers per year: 2023: 7; 2024: 5; 2025: 3.

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  1. 2023Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentationcited by 15
  2. 2023Scribble-Supervised RGB-T Salient Object Detectioncited by 8
  3. 2024Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentationcited by 8
  4. 2023Scribble-based 3D Multiple Abdominal Organ Segmentation via Triple-branch Multi-dilated Network with Pixel- and Class-wise Consistencycited by 7
  5. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  6. 20243D Vascular Segmentation Supervised by 2D Annotation of Maximum Intensity Projectioncited by 3
  7. 2024Scribble-Guided Diffusion for Training-free Text-to-Image Generationcited by 2
  8. 2025SparseMamba-PCL: Scribble-Supervised Medical Image Segmentation via SAM-Guided Progressive Collaborative Learningcited by 2
  9. 2023Volumetric Medical Image Segmentation via Scribble Annotations and Shape Priorscited by 1
  10. 2023Seeing Beyond Cancer: Multi-Institutional Validation of Object Localization and 3D Semantic Segmentation using Deep Learning for Breast MRIcited by 1
  11. 2024Revisiting 3D Medical Scribble Supervision: Benchmarking Beyond Cardiac Segmentationcited by 1
  12. 2023AttenScribble: Attentive Similarity Learning for Scribble-Supervised Medical Image Segmentationcited by 0
3 more
  1. 2024Skeleton Supervised Airway Segmentationcited by 0
  2. 2025DEPT: Deep Extreme Point Tracing for Ultrasound Image Segmentationcited by 0
  3. 2025Uncertainty-Aware Extreme Point Tracing for Weakly Supervised Ultrasound Image Segmentationcited by 0