# Scribble2D5: Weakly-Supervised Volumetric Image Segmentation via Scribble Annotations

arXiv: https://arxiv.org/abs/2205.06779 · HTML: https://mlsanity.com/paper/2205.06779/

## 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 (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1604.05144/) (2016)
- [Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/1803.10464/) (2018)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [Deep Extreme Cut: From Extreme Points to Object Segmentation](https://mlsanity.com/paper/1711.09081/) (2017)
- [Normalized Cut Loss for Weakly-supervised CNN Segmentation](https://mlsanity.com/paper/1804.01346/) (2018)
- [Weakly-Supervised Salient Object Detection via Scribble Annotations](https://mlsanity.com/paper/2003.07685/) (2020)
- [DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks](https://mlsanity.com/paper/1605.07866/) (2016)
- [CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision](https://mlsanity.com/paper/2203.01475/) (2022)
- [Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision](https://mlsanity.com/paper/2203.02106/) (2022)
- [Constrained-CNN losses for weakly supervised segmentation](https://mlsanity.com/paper/1805.04628/) (2018)
- [W-Net: A Deep Model for Fully Unsupervised Image Segmentation](https://mlsanity.com/paper/1711.08506/) (2017)
- [Going to Extremes: Weakly Supervised Medical Image Segmentation](https://mlsanity.com/paper/2009.11988/) (2020)
- [Scribble-based Domain Adaptation via Co-segmentation](https://mlsanity.com/paper/2007.03632/) (2020)
- [Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation](https://mlsanity.com/paper/2107.00583/) (2021)
- [ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentation](https://mlsanity.com/paper/2103.03664/) (2021)
- [Employing Weak Annotations for Medical Image Analysis Problems](https://mlsanity.com/paper/1708.06297/) (2017)

## Built on by (most cited first, 15 of 15)

- [Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation](https://mlsanity.com/paper/2303.13090/) (2023)
- [Scribble-Supervised RGB-T Salient Object Detection](https://mlsanity.com/paper/2303.09733/) (2023)
- [Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation](https://mlsanity.com/paper/2411.15763/) (2024)
- [Scribble-based 3D Multiple Abdominal Organ Segmentation via Triple-branch Multi-dilated Network with Pixel- and Class-wise Consistency](https://mlsanity.com/paper/2309.09730/) (2023)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [3D Vascular Segmentation Supervised by 2D Annotation of Maximum Intensity Projection](https://mlsanity.com/paper/2402.12128/) (2024)
- [Scribble-Guided Diffusion for Training-free Text-to-Image Generation](https://mlsanity.com/paper/2409.08026/) (2024)
- [SparseMamba-PCL: Scribble-Supervised Medical Image Segmentation via SAM-Guided Progressive Collaborative Learning](https://mlsanity.com/paper/2503.01633/) (2025)
- [Volumetric Medical Image Segmentation via Scribble Annotations and Shape Priors](https://mlsanity.com/paper/2310.08084/) (2023)
- [Seeing Beyond Cancer: Multi-Institutional Validation of Object Localization and 3D Semantic Segmentation using Deep Learning for Breast MRI](https://mlsanity.com/paper/2311.16213/) (2023)
- [Revisiting 3D Medical Scribble Supervision: Benchmarking Beyond Cardiac Segmentation](https://mlsanity.com/paper/2403.12834/) (2024)
- [AttenScribble: Attentive Similarity Learning for Scribble-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2312.06614/) (2023)
- [Skeleton Supervised Airway Segmentation](https://mlsanity.com/paper/2403.06510/) (2024)
- [DEPT: Deep Extreme Point Tracing for Ultrasound Image Segmentation](https://mlsanity.com/paper/2503.15260/) (2025)
- [Uncertainty-Aware Extreme Point Tracing for Weakly Supervised Ultrasound Image Segmentation](https://mlsanity.com/paper/2510.15666/) (2025)

## 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.