# Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation

arXiv: https://arxiv.org/abs/1911.02014 · HTML: https://mlsanity.com/paper/1911.02014/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2019.

## Built on by (most cited first, 30 of 32)

- [Weakly Supervised Object Localization and Detection: A Survey](https://mlsanity.com/paper/2104.07918/) (2021)
- [CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision](https://mlsanity.com/paper/2203.01475/) (2022)
- [ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation](https://mlsanity.com/paper/2206.02118/) (2022)
- [ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation](https://mlsanity.com/paper/2402.02029/) (2024)
- [ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding](https://mlsanity.com/paper/2307.16226/) (2023)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans](https://mlsanity.com/paper/2302.00162/) (2023)
- [Gaussian Dynamic Convolution for Efficient Single-Image Segmentation](https://mlsanity.com/paper/2104.08783/) (2021)
- [Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model using 3D Whole-body CT Scans](https://mlsanity.com/paper/2403.15063/) (2024)
- [ZScribbleSeg: Zen and the Art of Scribble Supervised Medical Image Segmentation](https://mlsanity.com/paper/2301.04882/) (2023)
- [Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation](https://mlsanity.com/paper/2411.15763/) (2024)
- [Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey](https://mlsanity.com/paper/2105.12694/) (2021)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Dense Regression Activation Maps For Lesion Segmentation in CT scans of COVID-19 patients](https://mlsanity.com/paper/2105.11748/) (2021)
- [Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection](https://mlsanity.com/paper/2403.12748/) (2024)
- [ModelMix: A New Model-Mixup Strategy to Minimize Vicinal Risk across Tasks for Few-scribble based Cardiac Segmentation](https://mlsanity.com/paper/2406.13237/) (2024)
- [Weakly Supervised Volumetric Segmentation via Self-taught Shape Denoising Model](https://mlsanity.com/paper/2104.13082/) (2021)
- [OCTAve: 2D en face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning with Locality Augmentation](https://mlsanity.com/paper/2207.12238/) (2022)
- [Leveraging point annotations in segmentation learning with boundary loss](https://mlsanity.com/paper/2311.03537/) (2023)
- [Cardiac Adipose Tissue Segmentation via Image-Level Annotations](https://mlsanity.com/paper/2206.04238/) (2022)
- [Synthesize Boundaries: A Boundary-aware Self-consistent Framework for Weakly Supervised Salient Object Detection](https://mlsanity.com/paper/2212.01764/) (2022)
- [Volumetric Medical Image Segmentation via Scribble Annotations and Shape Priors](https://mlsanity.com/paper/2310.08084/) (2023)
- [Revisiting 3D Medical Scribble Supervision: Benchmarking Beyond Cardiac Segmentation](https://mlsanity.com/paper/2403.12834/) (2024)
- [DuEDL: Dual-Branch Evidential Deep Learning for Scribble-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2405.14444/) (2024)
- [Facial Wrinkle Segmentation for Cosmetic Dermatology: Pretraining with Texture Map-Based Weak Supervision](https://mlsanity.com/paper/2408.10060/) (2024)
- [QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images](https://mlsanity.com/paper/2502.10294/) (2025)
- [MedCL: Learning Consistent Anatomy Distribution for Scribble-supervised Medical Image Segmentation](https://mlsanity.com/paper/2503.22890/) (2025)
- [Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images](https://mlsanity.com/paper/2211.05269/) (2022)
- [Size Aware Cross-shape Scribble Supervision for Medical Image Segmentation](https://mlsanity.com/paper/2408.13639/) (2024)
- [SP 3 : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation](https://mlsanity.com/paper/2411.11636/) (2024)

## Abstract (arXiv)

The recent state-of-the-art deep learning methods have significantly improved brain tumor segmentation. However, fully supervised training requires a large amount of manually labeled masks, which is highly time-consuming and needs domain expertise. Weakly supervised learning with scribbles provides a good trade-off between model accuracy and the effort of manual labeling. However, for segmenting the hierarchical brain tumor structures, manually labeling scribbles for each substructure could still be demanding. In this paper, we use only two kinds of weak labels, i.e., scribbles on whole tumor and healthy brain tissue, and global labels for the presence of each substructure, to train a deep learning model to segment all the sub-regions. Specifically, we train two networks in two phases: first, we only use whole tumor scribbles to train a whole tumor (WT) segmentation network, which roughly recovers the WT mask of training data; then we cluster the WT region with the guide of global labels. The rough substructure segmentation from clustering is used as weak labels to train the second network. The dense CRF loss is used to refine the weakly supervised segmentation. We evaluate our approach on the BraTS2017 dataset and achieve competitive WT dice score as well as comparable scores on substructure segmentation compared to an upper bound when trained with fully annotated masks.