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Endoscopy Out of Body Detection Application

Authors: Holoscan Team (NVIDIA)
Supported platforms: x86_64, aarch64
Last modified: March 18, 2025
Language: C++
Latest version: 1.0
Minimum Holoscan SDK version: 1.0.3
Tested Holoscan SDK versions: 2.9.0
Contribution metric: Level 1 - Highly Reliable

Endoscopy Out of Body Detection Workflow

Overview

This application performs real-time detection of whether an endoscope is inside or outside the body during endoscopic procedures. For each input frame, the application:

  • Classifies the frame as either "in-body" or "out-of-body"
  • Provides a confidence score for the classification
  • Outputs either to the console or to a CSV file (when analytics is enabled)

Note: This application does not include visualization components.

Quick Start

Run the following command to build and launch the application on a supported Holoscan platform:

./dev_container build_and_run endoscopy_out_of_body_detection --language <cpp/python>

Prerequisites

Data Requirements

Model and Sample Data

The endoscopy detection model and sample datasets are available on NGC. The package includes:

  • Pre-trained ONNX model for out-of-body detection: out_of_body_detection.onnx
  • Sample endoscopy video clips (MP4 format): sample_clip_out_of_body_detection.mp4

Data Preparation (optional)

The application requires the input videos to be converted to GXF tensor format. This conversion happens automatically during building, but manual conversion can be done following these steps:

  1. Download and extract the data:
unzip [NGC_DOWNLOAD].zip -d <data_dir>
  1. Convert the video to GXF tensor format using the provided script:
ffmpeg -i <INPUT_VIDEO_FILE> -fs 900M -pix_fmt rgb24 -f rawvideo pipe:1 | \
python convert_video_to_gxf_entities.py --width 256 --height 256 --channels 3 --framerate 30

Note: The conversion script (convert_video_to_gxf_entities.py) is available in the Holoscan SDK repository.

  1. Organize the data directory as follows:
data/
└── endoscopy_out_of_body_detection/
  ├── LICENSE.md
  ├── out_of_body_detection.onnx
  ├── sample_clip_out_of_body_detection.gxf_entities
  ├── sample_clip_out_of_body_detection.gxf_index
  └── sample_clip_out_of_body_detection.mp4

Configuration

The application uses endoscopy_out_of_body_detection.yaml for configuration. Key settings include:

  • Input video parameters in the replayer section
  • Model parameters in the inference section
  • Analytics settings for data export

Building

./dev_container build
./dev_container launch
./run build endoscopy_out_of_body_detection <cpp/python>
./run launch endoscopy_out_of_body_detection <cpp/python>

For more information, see the Holohub README.md.

Running the Application

Basic Usage

For C++: From your build directory:

applications/endoscopy_out_of_body_detection/endoscopy_out_of_body_detection \
  --config endoscopy_out_of_body_detection.yaml \
  --data ../data/endoscopy_out_of_body_detection

For Python:

applications/endoscopy_out_of_body_detection/endoscopy_out_of_body_detection.py \
  --config endoscopy_out_of_body_detection.yaml \
  --data ../data/endoscopy_out_of_body_detection

Analytics Mode

To enable analytics and export results to CSV:

  1. Set enable_analytics: true in the configuration file:
# endoscopy_out_of_body_detection.yaml
enable_analytics: true
  1. Configure analytics output (optional):
# Set output directory (default: current directory)
export HOLOSCAN_ANALYTICS_DATA_DIRECTORY="/path/to/output"

# Set output filename (default: data.csv)
export HOLOSCAN_ANALYTICS_DATA_FILE_NAME="results.csv"

The application will create:

  • A directory named after the application
  • Subdirectories with timestamps for each run
  • CSV files containing frame-by-frame classification results

Output Format

  • Console Mode: Displays "Likely in-body" or "Likely out-of-body" along with confidence scores for each frame.

  • Analytics Mode: Outputs a CSV file with frame-by-frame classification results in the following format:

In-body,Out-of-body,Confidence Score
1,0,0.972432
1,0,0.902066