Datasets:
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
video-object-segmentation
computer-vision
angiography
x-ray
medical-image-segmentation
video-segmentation
License:
| license: cc-by-nc-sa-4.0 | |
| task_categories: | |
| - object-detection | |
| - image-segmentation | |
| language: | |
| - en | |
| tags: | |
| - video-object-segmentation | |
| - computer-vision | |
| - angiography | |
| - x-ray | |
| - medical-image-segmentation | |
| - video-segmentation | |
| pretty_name: MOSXAV | |
| size_categories: | |
| - 10K<n<100K | |
| # <p align="center">MOSXAV: A Benchmark Dataset for Multi-Object Segmentation in X-ray Angiography Videos</p> | |
| <p align="center"> | |
| <a href="https://xilin-x.github.io/MOSXAV/" target="_blank"> | |
| <img alt="Static Badge" src="https://img.shields.io/badge/Homepage-MOSXAV-yellow?logo=databricks&logoColor=%23FF3621"> | |
| </a> | |
| </p> | |
| <p align="center"> | |
| <b>VOS Task:</b> | |
| | |
| <a href="https://arxiv.org/abs/2601.00988" target="_blank"> | |
| <img src="https://img.shields.io/badge/arXiv-2601.00988-b31b1b?style=flat&logo=arXiv&logoColor=%23B31B1B"> | |
| </a> | |
| | | |
| <b>Semantic Segmentation Task:</b> | |
| | |
| <a href="https://link.springer.com/chapter/10.1007/978-3-032-05472-2_2" target="_blank"> | |
| <img alt="Static Badge" src="https://img.shields.io/badge/SN-DGM4MICCAI 2025-f8f8f8?style=flat&labelColor=01324b"> | |
| </a> | |
| | |
| <a href="https://arxiv.org/abs/2507.16429" target="_blank"> | |
| <img src="https://img.shields.io/badge/arXiv-2507.16429-b31b1b?style=flat&logo=arXiv&logoColor=%23B31B1B"> | |
| </a> | |
| </p> | |
| <p align="center"> | |
| <b>VOS Task Evaluation Code:</b> | |
| | |
| <a href="https://github.com/xilin-x/xavos-eval" target="_blank"> | |
| <img alt="Static Badge" src="https://img.shields.io/badge/Evaluation-XAVOS--Eval-green"> | |
| </a> | |
| </p> | |
| --- | |
| ## 📖 1. Overview | |
| **MOSXAV** is a benchmark dataset designed for **multi-object segmentation** in X-ray angiography videos. It provides **high-quality, manually annotated segmentation ground truth**, supporting the analysis of vascular structures in dynamic medical imaging. Each video contains **33∼70** frames at a resolution of **512×512 pixels**. Vascular regions are annotated by experienced radiologists, with annotations focused on **one or two key frames** where the contrast agent is most prominent. | |
| - The **training and validation sets** include **30 sequences** (2,335 frames), with **annotations every 5 frames**. | |
| - The **test set** consists of **12 sequences** (488 frames), with **frame-level annotations** throughout. | |
| MOSXAV provides a valuable resource for the development and benchmarking of methods in X-ray angiography video segmentation. | |
| --- | |
| ## 🛠️ 2. Annotation Protocol | |
| To ensure high-quality and anatomically accurate labels, we implemented a rigorous, multi-stage annotation workflow. This process combined the efficiency of deep learning with the precision of manual expert refinement. The protocol consisted of four primary phases: | |
| * **Annotator Training:** Annotators were trained on a specialized subset of images to standardize their understanding of anatomical structures and specific labeling guidelines. | |
| * **Semi-Automated Initialization:** We utilized a semi-automated approach to generate initial segmentation masks. This was powered by the [PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg) framework, leveraging models pre-trained on extensive image and video datasets to provide a robust baseline. | |
| * **Expert Revision:** Human annotators meticulously reviewed the AI-generated masks. This involved careful delineation of vessel boundaries and manual adjustments to correct any discrepancies in the automated output. | |
| * **Consensus & Quality Assurance:** To maintain consistency, a final review and consensus-building phase were conducted, ensuring that all labels met our strict quality benchmarks. | |
| --- | |
| ## 📊 3. Object Categories and Statistics | |
| The **MOSXAV dataset** is designed to support two distinct medical imaging challenges: Video Object Segmentation (VOS) and Multi-class Semantic Segmentation. | |
| ### 3.1 Video Object Segmentation (VOS) | |
| The VOS task focuses on the temporal tracking and segmentation of coronary arteries as they are opacified by contrast agents. This task requires high temporal consistency across video sequences: | |
| * **Objective**: Segmenting coronary arteries filled with contrast agents throughout the cardiac cycle. | |
| * **Object Density**: **Train & Val Sets:** Up to 5 individual objects per sequence. **Test Set:** Increased complexity with up to 10 individual objects per sequence to evaluate model scalability and robustness. | |
| ### 3.2 Semantic Segmentation | |
| The semantic segmentation task targets the simultaneous identification of critical intervention tools and anatomical features. We define four primary categories: | |
| | Category | Label ID | Color Preview | RGB Color | Description | | |
| | :--- | :---: | :---: | :---: | :--- | | |
| | **Background** | 0 |  | `[0,0,0]` | All pixels not belonging to the below classes, including the spine, ribs, diaphragm, and image noise. | | |
| | **Vessel** | 1 |  | `[85,170,255]` | The primary coronary anatomy under observation. | | |
| | **Contrast Catheter** | 2 |  | `[170,255,0]` | The specific catheter used for dye injection. | | |
| | **Catheter** | 3 |  | `[249,193,0]` | General-purpose intervention or diagnostic catheters. | | |
| | **Balloon** | 4 |  | `[255,0,0]` | Angioplasty balloons used during interventional procedures. | | |
| | **Others** | 5 |  | `[244,108,59]` | Other category. | | |
| ### 3.3 File Structure | |
| The MOSXAV dataset is organized into a hierarchical directory structure to support both video-level (VOS) and frame-level (Semantic Segmentation) tasks. The data is split into ```trainval```, and ```test``` directories, each containing the original sequences and their corresponding pixel-level annotations. | |
| ```text | |
| MOSXAV_Dataset/ | |
| ├── trainval/ | |
| │ ├── Annotations/ # VOS instance masks (unique ID per branch) | |
| │ │ └── v00/ # Sequence folder | |
| │ │ ├── 00000.png # Frame-wise instance mask | |
| │ │ └── ... | |
| │ ├── Annotations_Semantic/ # Multi-class semantic masks (Label IDs 0-4) | |
| │ │ └── v00/ | |
| │ │ ├── 00000.png | |
| │ │ └── ... | |
| │ ├── JPEGImages/ # Raw X-ray Angiography frames | |
| │ │ └── v00/ | |
| │ │ ├── 00000.jpg | |
| │ │ └── ... | |
| │ ├── ImageSets/ # Split lists and first-frame metadata | |
| │ │ ├── train.txt | |
| │ │ ├── val.txt | |
| │ │ └── val_first_mask.json # Frame ID of each object's first appearance | |
| │ └── labels.json # Global category metadata | |
| └── test/ | |
| ├── Annotations/ | |
| ├── Annotations_Semantic/ # Multi-class semantic masks (Label IDs 0-5) | |
| ├── JPEGImages/ | |
| ├── ImageSets/ | |
| │ ├── test.txt | |
| │ └── test_first_mask.json # Frame ID of each object's first appearance, along with the seen and unseen object classes in the training set | |
| └── labels.json | |
| ``` | |
| --- | |
| ## 📥 4. Download | |
| The MOSXAV Dataset is hosted across multiple cloud storage platforms to ensure accessibility and high download speeds globally. | |
| | Source | Download Link | Extraction Code / Notes | | |
| | :---: | :---: | :---: | | |
| | **OneDrive** | [🌐 Click Here](https://1drv.ms/f/c/05b6df5b859ecdde/IgBLUOUDkt5bQ4S80HI2Gb_7AZW-uXoxuXuFPXCrwOUrBPo?e=6fBF22) |  | | |
| | **Google Drive** | [🌐 Click Here](https://drive.google.com/drive/folders/1d-kOWF7TkXqkRAfmh0ugkEvT9NmXr3cP?usp=sharing) | - | | |
| | **Baidu Pan** | [🌐 Click Here](https://pan.baidu.com/s/1-1i6nljdrdp90tMg30bZwQ) |  | | |
| --- | |
| ## ⚖️ 5. License | |
| The dataset is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/). See [LICENSE](./LICENSE) for details. | |
| <br> | |
| <p align="left"> | |
| <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"> | |
| <img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /> | |
| </a> | |
| </p> | |
| --- | |
| ## 👥 Contributors | |
| - Principal investigator: [YingLiangUEA](https://github.com/YingLiangUEA) | |
| - Core contributors: [xilin-x](https://github.com/xilin-x), [YingLiangUEA](https://github.com/YingLiangUEA) | |
| - Student contributors: [EthanKoland](https://github.com/EthanKoland) | |
| --- | |
| ## 📝 Citation | |
| Please consider citing MOSXAV if it helps your research. | |
| ```bibtex | |
| @article{FSVOSXA, | |
| title={Few-Shot Video Object Segmentation in X-Ray Angiography Using Local Matching and Spatio-Temporal Consistency Loss}, | |
| author={Xi, Lin and Ma, Yingliang and Zhuang, Xiahai}, | |
| journal={arXiv preprint arXiv:2601.00988}, | |
| year={2026} | |
| } | |
| ``` | |
| ```bibtex | |
| @InProceedings{RNPLL, | |
| author={Xi, Lin and Ma, Yingliang and Wang, Cheng and Howell, Sandra and Rinaldi, Aldo and Rhode, Kawal S.}, | |
| title={Robust Noisy Pseudo-Label Learning for Semi-supervised Medical Image Segmentation Using Diffusion Model}, | |
| booktitle={Deep Generative Models Workshop, International Conference on Medical Image Computing and Computer-Assisted Intervention (DGM4MICCAI)}, | |
| year={2026}, | |
| pages={12--23} | |
| } | |
| ``` | |
| --- | |
| ## ✉️ Contact | |
| For questions or feedback, please contact: | |
| * Lin Xi:  | |
| * Yingliang Ma:  | |
| --- | |
| <p align="center"> | |
| Copyright © 2026 MOSXAV Project Team. All rights reserved. | |
| </p> |