Welcome to YaDV (Yet another DICOM Viewer)

Platform to open, view, analyze, classify and enrich DICOM format images..

Glossary

Term	Description
Radiology	Radiology is a branch of medicine that uses imaging technology to diagnose and treat disease
Radiologists	Radiologist are medical doctors that specialize in diagnosing and treating injuries and diseases using medical imaging (radiology) procedures (exams/tests) such as X-rays, computed tomography (CT), ultrasound etc.
PACS	Picture Archiving and Communication System: PACS is a high-speed, graphical, computer network system for the storage, recovery, and display of radiologic images
HL7	Health Level Seven International : HL7 is a set of international standards used to transfer and share data between various healthcare providers
DICOM	Digital Imaging and COmmunications in Medicine – an universal standard for Digital Imaging
NEMA	National Electronic Manufacturing Association - An ANSI-accredited Standards Developing Organization. The DICOM Standard is managed by the Medical Imaging & Technology Alliance - a division of the NEMA.

About YaDV

YaDV is a web-based DICOM Viewer for PACS enables Radiologist to diagnoses, viewing, and transmitting medical images.

What problem will YaDV solves?

• YaDV is an DICOM viewer greatly facilitates the day-to-day of cardiologists, traumatologist, oncologists, etc. and most importantly, it improves the healthcare and service to patients.
• YaDV support different image processing abilities and advanced functions. Besides image visualisation YaDV can take measurements and convert images to other formats.
• YaDV provides 3D image viewing for the surgical planning What makes YaDV different: One stop solution for COVID Analysis DICOM format, 3D object view, Image Classification and in future will add hand gesture based DICOM image processing.

DICOM Overview

• DICOM stands for Digital Imaging and COmmunications in Medicine – an universal standard for Digital Imaging.
• DICOM is a specification for the creation, transmission, and storage of digital medical image and report data.
• Another important acronym that seemingly all DICOM vendors plug into their names is PACS (Picture Archiving and Communication Systems).
• PACS are medical systems (consisting of necessary hardware and software) built to run digital medical imaging. They comprise:
• • Modalities: Digital image acquisition devices, such as CT scanners or ultrasound.
• • Digital image archives: Where the acquired images are stored.
• • Workstations: Where radiologists view (“read”) the images. (YaDV)

YaDV Architecture

YaDV Module Structure

Core Capabilities

1. Load and View DICOM Image
2. View DICOM Head Metadata Information
3. Basic DICOM Image Operation – i.e. Rotation, Zoom, Measurement etc.
4. Image Classification
5. 3D image rendering using Marching Cube
6. Anonymize during export
7. Cloud Deployed (Demo purpose deployed in AWS EC2 instance)

Prerequisites

Make sure you have installed all of the following prerequisites on your development machine:

• Git - Download & Install Git. OSX and Linux machines typically have this already installed.
• Python - Download & Install Python3 - Minimum requirement 3.8.x

Installation

YaDV is based on streamlit and streamlit can also be installed in a virtual environment on Windows, Mac and Linux.

pip install -r requirement.txt

[OR]

pip install streamlit
pip install numpy
pip install fastai
pip install -Uqq fastbook
pip install pytorch
pip install streamlit-drawable-canvas
pip install cryptohash
pip install pandas
pip install pydicom
pip install opencv-python
pip install skimage
pip install matplotlib
pip install plotly
pip install mplot3d-dragger
pip install graphviz
pip install scikit-image

Running YaDV

YaDV can be executed used following command:

cd yadv-dicom-imageprocessing
mkdir overlayimage
mkdir Exportfile
streamlit run yadv.py

Development

git clone https://github.com/prahalad12345/yadv-dicom-imageprocessing.git

3rd Party Library Dependencies

YaDV uses following 3rd party tools/libraries:

3rd Party	Reference Link
OpenCV	https://opencv.org/
PyDICOM	https://pydicom.github.io/
Fast.ai	https://www.fast.ai/
Streamlit	https://streamlit.io/
Numpy	https://numpy.org/
Pandas	https://pandas.pydata.org/
Matplot	https://matplotlib.org/
Mplot3d	https://matplotlib.org/
Plotly	https://plotly.com/
SkImage	https://scikit-image.org/
Cryptohash	https://www.cryptohash.net/

Next Step

1. The Value Of Interest Lookup (VOI LUT - 0028, 3010) Table Implementation
2. Hand Gesture based Image operation - End Goal (New viewer different from YaDV tech stack)
3. Open multiple image in parallel and compare
4. Predict COVID cases based on CT Images
5. Predict COVID cases based on XRays
6. Integration with PACS
7. Scan patient, auto face detect and pull patient history from PACS
8. Advance measurement overlay on top of images

Reference

Topic	Reference Link
Digital Image Communication in Medicine	Book from Oleg S. Pianykh
National Electrical Manufacturers Association (NEMA)	http://dicom.nema.org/medical/dicom/current/output/chtml/part10/chapter_7.html
Python library for DICOM	https://pypi.org/project/pydicom/
FAST.AI for Medical Image Processing	https://docs.fast.ai/medical.imaging
User Interface for Image Viewer	https://streamlit.io/
Heroku Deployment	https://towardsdatascience.com/deploying-a-basic-streamlit-app-to-heroku-be25a527fcb3
AWS Deployment	https://towardsdatascience.com/how-to-deploy-a-streamlit-app-using-an-amazon-free-ec2-instance-416a41f69dc3

FAQ

1. If you get "ImportError: libGL.so.1: cannot open shared object file" error message install following package:

   sudo apt install libgl1-mesa-glx

License

YaDV is completely free and open-source