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Legacy example · C++ · Python · Version 1.0

In-Out Body Detection

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Authors	Jasper Hofman (Orsi Academy)
Supported platforms	x86_64, aarch64
Language	C++, Python
Version	1.0
Compatibility	HSDK 0.6
Contribution level	Level 3 · Features under development
Source	HoloHub ↗

Legacy HoloHub application

This application comes from HoloHub and was last tested with Holoscan SDK (HSDK) 0.6. It runs with the HoloHub CLI, ./holohub.

Fig. 1: Example of anonymized result after inference

Introduction

In robotic surgery, anonymization of video is necessary to ensure privacy and protect patient data. During e.g. swapping of robotic tools or lens cleaning the endoscope is removed from the body, possibly capturing faces, recognizable tattoos or written patient data. Therefore, going out of body is a suiting definition for an anonymization boundary.

Pipeline

Fig. 2: Schematic overview of Holoscan application

Towards realtime anonymization, a binary out-of-body classifier was trained and deployed using Holoscan platform. Figure 2 shows a schematic overview of the application. After capturing the frame, the alpha channel is dropped by the Format Converter operator. Additionally, the black padding borders added by the robotic system are removed, the tensor is resized to 512x512 pixels and the tensor type is converted from int [0, 255] to float [0, 1]. In the Anonymization Preprocessor operator the tensor pixel values of every collor channel are normalized using the corresponding means and standard deviations of the anonymization dataset.

After model inference with the Multi-AI inference operator, the result frame is anonymized in the Orsi Visualizer operator according to the model output. The blurring is applied using a glsl program.

Controls

Action	Control
Enable anonymization	B

Build and Launch app

C++

./holohub run orsi_in_out_body --language cpp

Python

./holohub run orsi_in_out_body --language python

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