Skip to content

NadaElmasry/Cephalometric-Landmark-Detection

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

9 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Cepheo

Cepheo is an innovative DL driven online orthodontic and orthognathic platform for dental clinicians.

Description

We developed a node.js and flask APIs connected to MongoDb database, through which we deployed the best model. It automatically performs landmark positioning. It can be used as an online diagnostic software that reduces the time and effort of orthodontists involved in the orthodontic case and diagnosis.

Logo

🚀 Features

  • The dentical clinician can add new patient via upload page.
  • View the all patients data in one page in the repository part.
  • Through the repository section also the dentical clinician can also update, delete the medical record for each patient.
  • After the analysis is done, the patient record can be saved as a PDF report or image.
  • We also provide in our platform help page that contains frequently asked questions and short instruction video in our youtube channel to provide information of everthing till getting analysis done.

🖥️ Tech Stack

Client: HTML, CSS, JS

Server: Node, MongoDB, Flask

Model: UNet, Inception, Resnet

📂 Dataset

Detection of cephalometric landmarks with Deep learning models. Dataset:

  • 400 X-ray images each image contains 19 landmarks.
  • Excel sheets containg X and Y positions for each landmark in each image.

Landmark detection

Models Notebook

  • Binary Mask Trial Unet.ipynb The mask used for segmentation is a binary 1 channel array, where 1's represent landmarks location and 0's represent the background.

  • Unet_and_get_ROIs_for_Stage2.ipynb The mask used for segmentation is a 20 channels mask, where each channel represent a landmark and the last channel represent the background. Each channel was filled using the following equation.

Home Page Section

  • resnet_inception_final.ipynb

  • resnet_rois.ipynb

Installation

Install node.js 👉 Download

To use the Model 👉 Download

Dependencies

Install

  • FlaskAPI: Flask, TensorFlow, OpenCv, Numpy
  • NodeAPI: npm install command

Run Locally

Start the node server

  node app.js

Add model in models folder then start flask server

  run main.py

Start the client

  run index.html

🛠 Skills

HTML, CSS, Javascript, Node, Flask, MongoDB and deep learning

Screenshots

Cepheo - Home Page

Home Page Section

Cepheo - Upload Section

Upload Page Section

Cepheo - Repository Section

Repository Page Section

Cepheo - Result Section

Result Page Section

  • Sample of saved analyzed image

analyzed image

  • Sample of our patient medical record Patient Record

Cepheo - Help Page

Help Page Section

⚡ Demo

That is a short instruction video on our youtube channel to learn everything until the analysis is done!

Watch the video

Support

For support, email [email protected] or join our community on facebook.

Links

YouTube Facebook

Feedback

If you have any feedback, please reach out to us at [email protected]

Authors

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published