face_deid_ct

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Function to de-identify the face of patients in head CTs

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# face_deid_ct
Package to de-identify the face of patients in head CTs

# Drown the volume of a head CT

The `drown_volume` function is inspired by two ideas: (i) eroding the skin and subcutaneous fat of the head and (ii) replacing the air around the head with a customizable pixel value. The analogy with drowning comes from the equivalence of filling the air around the patient with other pixel densities. These two ideas would probably be enough individually to de-identify the face. We proposed both to be used together to provide a more robust deidentification.

We believe filling the air with pixel densities similar to the ones found in the skin is the best way to blur the face. The reason is there is no way to window the 3D rendering in a way to show the face. If you do not remove the 'skin Hounsfield units,' the 'air volume' is not transparent. If you window to remove the 'air volume', then you automatically remove the face.

There is a concern that altering the air pixels might influence ML models. We believe it might influence if the training and testing data are processed in different way. However, if the training and testing data are processed the same way, we believe this pixels alteration will not reduce model performance.

This function was written with the help of GPT4.

This code is an adaptation from the idea in this [nice paper](https://pubs.rsna.org/doi/10.1148/radiol.2020192617) published in [_Radiology_](https://pubs.rsna.org/journal/radiology).

## Demo

Check out the demo app [here](https://felipekitamura-face-deid-ct.hf.space).

## Example

```python
from face_deid_ct import drown_volume

path_to_dicom_files = "/mnt/d/HeadCT"

output_path = "/mnt/d/HeadCT_deid"

drown_volume(in_path = path_to_dicom_files,
             out_path = output_path,
             replacer='face')
```

![Example of the deidentification options compared to the original CT](https://github.com/kitamura-felipe/face_deid_ct/blob/main/face_deid_ct_hair.jpg?raw=true)

## Parameters:

`in_path` (str): The path to the directory containing the input DICOM files.

`out_path` (str, optional): The path to the directory where the output DICOM files will be saved. If not provided, the output files will be saved in the input directory appended by "_d".

`replacer` (str, optional): Indicates what kind of pixels are going to be replaced. Default is 'face'.

'face': replaces air and face with random values that are found in the skin and subcutaneous fat. This is the safest one to hide the face.

'air': replaces air and face with -1000 HU.

int: replaces air and face with int HU.

**Returns:**

The function does not return any value. Instead, it saves new DICOM files in the specified or default directory and prints the total elapsed time of the operation.

## How does it work?

The `drown_volume` function is a Python function designed to process DICOM files from a specified directory. The function performs several operations including binarization, retrieving the largest connected component, dilation, and applying a mask. Following these operations, random values are applied to the dilated volume based on a unique values list which is obtained from the masked volume.

## Requirements
```python
pydicom
numpy
opencv-python
matplotlib
tqdm
jupyter
python-gdcm
```

Requirements are listed also in the `requirements.txt` file.

## Contribution
Feel free to fork the project, submit issues, or make pull requests.

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