metadata

license: cc-by-nc-sa-4.0
language:
  - en
tags:
  - action
  - segmentation
size_categories:
  - 100K<n<1M

CathAction Dataset

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.

Please fill out the download form and agree to our license prior to downloading the dataset.

Dataset Structure:

1. Catheterization Action understanding

The CathAction dataset encompasses annotated frames for catheterization action understanding task such as catheterization anticipation and action recognition.

These are five classes: advance catheter, retract catheter, advance guidewire, retract guidewire, and rotate.

Annotation Files Structure

The groundtruth CSV file containing 6 columns:

Column Name	Type	Example	Description
`video_id`	string	`video_1`	Video the segment is in
`start_frame`	int	`430`	Start frame of the action
`stop_frame`	int	`643`	End frame of the action
`all_action_classes`	list of int (1 or more)	`[1]`	List of numeric IDs corresponding to all of the parsed Action' classes.

2. Collision Detection

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.

The dataset is organized as follows:

images/: Contains images related to collision and normal events.
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
train_phantom.txt: A text file listing paths to training images and labels for the "phantom" data source in the collision detection task.
valid_animal.txt: A text file listing paths to validation images and labels for the "animal" source data.
valid_phantom.txt: A text file listing paths to validation images and labels for the "phantom" source data.

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.

Usage

Training: Use train_phantom.txt to load training data for the phantom category.
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.

This structure supports streamlined data loading and management for training, validating, and testing collision detection algorithms.

For more information, please visit our webpage.

For inquiries or assistance, please contact the authors at this link.

Best regards,

Authors