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--- |
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license: cc-by-nc-sa-4.0 |
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language: |
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- en |
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tags: |
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- action |
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- segmentation |
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size_categories: |
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- 100K<n<1M |
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--- |
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# CathAction Dataset |
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CathAction is large-scale dataset designed for advancing catheterization understanding. CathAction comprises annotated frames focused on catheterization understanding and collision detection, along with groundtruth masks dedicated to catheter and guidewire segmentation. |
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Please fill out the [download form](https://airvlab.github.io/cathaction/docs/download/) and agree to our license prior to downloading the dataset. |
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# Dataset Structure: |
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## 1. Catheterization Action understanding |
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The CathAction dataset encompasses annotated frames for catheterization action understanding task such as catheterization anticipation and action recognition. |
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These are five classes: *advance catheter*, *retract catheter*, *advance guidewire*, *retract guidewire*, and *rotate*. |
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The dataset is organized into the following folders and files: |
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- **video_frames/**: Contains extracted video frames for each video. |
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- **feature_extractions/**: Contains pre-extracted RGB features, extracted using [this code](https://github.com/yjxiong/tsn-pytorch). |
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- **training.csv**: Groundtruth CSV file for training data. |
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- **validation.csv**: Groundtruth CSV file for validation data. |
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### Annotation File Structure |
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The annotation files (`training.csv` and `validation.csv`) contain four columns, with the following structure: |
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| Column Name | Type | Example | Description | |
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|---------------------|------------------|--------------|-------------------------------------------------------------------------------------------------| |
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| `video_id` | string | `video_1` | ID of the video where the action segment is located. | |
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| `start_frame` | int | `430` | Start frame of the action. | |
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| `stop_frame` | int | `643` | End frame of the action. | |
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| `all_action_classes`| list of int(s) | `[1]` | List of numeric IDs for all detected action classes in the segment. | |
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The frames and pre-extracted RGB features are located in the `video_frames` and `feature_extractions` folders, respectively, and can be generated using [this code](https://github.com/yjxiong/tsn-pytorch). |
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### Usage |
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1. **Catheterization Action Recognition and Anticipation Models**: Use the `start_frame` and `stop_frame` values, along with the ground truth `all_action_classes` in the CSV file, to train models that recognize action segments and anticipate future catheter actions. |
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## 2. Collision Detection |
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The CathAction dataset is designed for the collision detection task, which involves identifying whether the tip of the catheter or guidewire comes into contact with the blood vessel wall. |
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The dataset is organized as follows: |
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- **images/**: Contains images related to collision and normal events. |
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- **labels/**: Contains annotation files for each image, detailing information on bounding boxes and object classes, including collision occurrences and the normal class for the corresponding image |
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- **train_phantom.txt**: A text file listing paths to training images and labels for the "phantom" data source in the collision detection task. |
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- **valid_animal.txt**: A text file listing paths to validation images and labels for the "animal" source data. |
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- **valid_phantom.txt**: A text file listing paths to validation images and labels for the "phantom" source data. |
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Each `.txt` file contains a list of image and label paths for its respective category and split (train/validation), enabling easy access and organization for model training and evaluation. |
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### Usage |
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1. **Training**: Use `train_phantom.txt` to load training data for the phantom data source. |
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2. **Validation**: Use `valid_animal.txt` and `valid_phantom.txt` for validating model performance on different data sources, specifically focusing on the 'animal' and 'phantom' data. |
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For more information, please visit our [webpage](https://airvlab.github.io/cathaction/). |
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For inquiries or assistance, please contact the authors at [this link](https://airvlab.github.io/cathaction/). |
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Best regards, |
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Authors |
