# The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge

arXiv: https://arxiv.org/abs/2301.03281 · HTML: https://mlsanity.com/paper/2301.03281/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 49 references that are in the map.
- First posted on arXiv 9 Jan 2023.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge](https://mlsanity.com/paper/1912.01054/) (2019)
- [Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks](https://mlsanity.com/paper/1904.10030/) (2019)
- [3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes](https://mlsanity.com/paper/1711.08580/) (2017)
- [Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge](https://mlsanity.com/paper/2108.04016/) (2021)
- [Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation](https://mlsanity.com/paper/1609.01006/) (2016)
- [3D Consistent & Robust Segmentation of Cardiac Images by Deep Learning with Spatial Propagation](https://mlsanity.com/paper/1804.09400/) (2018)
- [PatchFCN for Intracranial Hemorrhage Detection](https://mlsanity.com/paper/1806.03265/) (2018)
- [Automated segmentation of intracranial hemorrhages from 3D CT](https://mlsanity.com/paper/2209.10648/) (2022)

## Built on by (most cited first, 26 of 26)

- [Large-Vocabulary Segmentation for Medical Images with Text Prompts](https://mlsanity.com/paper/2312.17183/) (2023)
- [SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks](https://mlsanity.com/paper/2311.11969/) (2023)
- [nnInteractive: Redefining 3D Promptable Segmentation](https://mlsanity.com/paper/2503.08373/) (2025)
- [Foundation Models for Biomedical Image Segmentation: A Survey](https://mlsanity.com/paper/2401.07654/) (2024)
- [BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset](https://mlsanity.com/paper/2308.11298/) (2023)
- [MedSG-Bench: A Benchmark for Medical Image Sequences Grounding](https://mlsanity.com/paper/2505.11852/) (2025)
- [CenterMamba-SAM: Center-Prioritized Scanning and Temporal Prototypes for Brain Lesion Segmentation](https://mlsanity.com/paper/2511.01243/) (2025)
- [MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applications](https://mlsanity.com/paper/2410.15432/) (2024)
- [SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage Segmentation](https://mlsanity.com/paper/2311.08190/) (2023)
- [SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Prompts](https://mlsanity.com/paper/2403.13258/) (2024)
- [Transformers-based architectures for stroke segmentation: A review](https://mlsanity.com/paper/2403.18637/) (2024)
- [Voxel Scene Graph for Intracranial Hemorrhage](https://mlsanity.com/paper/2407.21580/) (2024)
- [LesionDiffusion: Towards Text-controlled General Lesion Synthesis](https://mlsanity.com/paper/2503.00741/) (2025)
- [Automated Real-time Assessment of Intracranial Hemorrhage Detection AI Using an Ensembled Monitoring Model (EMM)](https://mlsanity.com/paper/2505.11738/) (2025)
- [Detection of Intracranial Hemorrhage for Trauma Patients](https://mlsanity.com/paper/2408.10768/) (2024)
- [Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging](https://mlsanity.com/paper/2503.19005/) (2025)
- [Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach](https://mlsanity.com/paper/2506.03238/) (2025)
- [Benchmarking Class Activation Map Methods for Explainable Brain Hemorrhage Classification on Hemorica Dataset](https://mlsanity.com/paper/2508.17699/) (2025)
- [Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development](https://mlsanity.com/paper/2603.27460/) (2026)
- [Class Activation Map-based Weakly supervised Hemorrhage Segmentation using Resnet-LSTM in Non-Contrast Computed Tomography images](https://mlsanity.com/paper/2309.16627/) (2023)
- [Examining Deployment and Refinement of the VIOLA-AI Intracranial Hemorrhage Model Using an Interactive NeoMedSys Platform](https://mlsanity.com/paper/2505.09380/) (2025)
- [Federated Voxel Scene Graph for Intracranial Hemorrhage](https://mlsanity.com/paper/2411.00578/) (2024)
- [SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2411.14525/) (2024)
- [Modality-Projection Universal Model for Comprehensive Full-Body Medical Imaging Segmentation](https://mlsanity.com/paper/2412.19026/) (2024)
- [M^3Builder: A Multi-Agent System for Automated Machine Learning in Medical Imaging](https://mlsanity.com/paper/2502.20301/) (2025)
- [3D Medical Imaging Segmentation on Non-Contrast CT](https://mlsanity.com/paper/2503.08361/) (2025)

## Abstract (arXiv)

Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores the anisotropic nature of the NCCT, and are evaluated on different in-house datasets with distinct metrics, making it highly challenging to improve segmentation performance and perform objective comparisons among different methods. The INSTANCE 2022 was a grand challenge held in conjunction with the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). It is intended to resolve the above-mentioned problems and promote the development of both intracranial hemorrhage segmentation and anisotropic data processing. The INSTANCE released a training set of 100 cases with ground-truth and a validation set with 30 cases without ground-truth labels that were available to the participants. A held-out testing set with 70 cases is utilized for the final evaluation and ranking. The methods from different participants are ranked based on four metrics, including Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), Relative Volume Difference (RVD) and Normalized Surface Dice (NSD). A total of 13 teams submitted distinct solutions to resolve the challenges, making several baseline models, pre-processing strategies and anisotropic data processing techniques available to future researchers. The winner method achieved an average DSC of 0.6925, demonstrating a significant growth over our proposed baseline method. To the best of our knowledge, the proposed INSTANCE challenge releases the first intracranial hemorrhage segmentation benchmark, and is also the first challenge that intended to resolve the anisotropic problem in 3D medical image segmentation, which provides new alternatives in these research fields.