Update README.md

README.md

@@ -13,4 +13,167 @@ tags:
 - 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>
+  &nbsp;&nbsp;
+  <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>
+  &nbsp;&nbsp; | &nbsp;&nbsp;
+  <b>Semantic Segmentation Task:</b>
+  &nbsp;&nbsp;
+  <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>
+  &nbsp;
+  <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>
+
+---
+
+## 📖 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&sim;70** frames at a resolution of **512&times;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 | ![](https://img.shields.io/badge/-%20-000000?style=flat-square) | `[0,0,0]` | All pixels not belonging to the below classes, including the spine, ribs, diaphragm, and image noise. |
+| **Vessel** | 1 | ![](https://img.shields.io/badge/-%20-55AAFF?style=flat-square) | `[85,170,255]` | The primary coronary anatomy under observation. |
+| **Contrast Catheter** | 2 | ![](https://img.shields.io/badge/-%20-AAFF00?style=flat-square) | `[170,255,0]` | The specific catheter used for dye injection. |
+| **Catheter** | 3 | ![](https://img.shields.io/badge/-%20-F9C100?style=flat-square) | `[249,193,0]` | General-purpose intervention or diagnostic catheters. |
+| **Balloon** | 4 | ![](https://img.shields.io/badge/-%20-FF0000?style=flat-square) | `[255,0,0]` | Angioplasty balloons used during interventional procedures. |
+| **Others** | 5 | ![](https://img.shields.io/badge/-%20-F46C3B?style=flat-square) | `[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     # Each object's first appearance frame ID
+│   └── 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    # Each object's first appearance frame ID
+    └── 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) | ![Code PNG](docs/_static/images/onedrive.png) |
+| **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) | ![Code PNG](docs/_static/images/baidupan.png) |
+
+---
+
+## ⚖️ 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>
+
+---
+
+## 📝 Citation
+
+Please consider to cite MOSXAV if it helps your research.
+
+```bibtex
+@InProceedings{10.1007/978-3-032-05472-2_2,
+    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: ![Code PNG](docs/_static/images/email1.png)
+* Yingliang Ma: ![Code PNG](docs/_static/images/email2.png)
+
+---
+<p align="center">
+  Copyright © 2026 MOSXAV Project Team. All rights reserved.
+</p>