https://github.com/vishal815/team-logiclegends-proxmed-hackathon-hypodense-segmentation-ai-project-

Project Overview:- The objective of this hackathon challenge is to develop a robust and efficient algorithm or AI model capable of accurately segmenting the hypodense region from Brain Non-Contrast Computed Tomography (NCCT) images. The primary goal is to automate and streamline the identification of early ischemic changes in acute stroke patients.
https://github.com/vishal815/team-logiclegends-proxmed-hackathon-hypodense-segmentation-ai-project-

ai datascience datavisualization deeplearning githubprojects hackathon hackathon-project healthtech imagesegmentation kaggle machinelearning medicalimaging neuralnetworks proxmed python segmentation-based-detection

Last synced: 2 months ago
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Project Overview:- The objective of this hackathon challenge is to develop a robust and efficient algorithm or AI model capable of accurately segmenting the hypodense region from Brain Non-Contrast Computed Tomography (NCCT) images. The primary goal is to automate and streamline the identification of early ischemic changes in acute stroke patients.

• Host: GitHub
• URL: https://github.com/vishal815/team-logiclegends-proxmed-hackathon-hypodense-segmentation-ai-project-
• Owner: vishal815
• License: apache-2.0
• Created: 2023-12-10T03:02:15.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-12-10T05:56:03.000Z (over 1 year ago)
• Last Synced: 2025-01-30T20:57:04.040Z (4 months ago)
• Topics: ai, datascience, datavisualization, deeplearning, githubprojects, hackathon, hackathon-project, healthtech, imagesegmentation, kaggle, machinelearning, medicalimaging, neuralnetworks, proxmed, python, segmentation-based-detection
• Language: Jupyter Notebook
• Homepage: https://colab.research.google.com/github/vishal815/Team-LogicLegends-Proxmed-Hackathon-Hypodense-Segmentation-AI-Project-/blob/main/Hypodense-Segmentation-AI.ipynb
• Size: 1.45 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Team LogicLegends Proxmed Hackathon Hypodense Segmentation AI Project.

[👆click on this link to open Project in Google Colab](https://colab.research.google.com/github/vishal815/Team-LogicLegends-Proxmed-Hackathon-Hypodense-Segmentation-AI-Project-/blob/main/Hypodense-Segmentation-AI.ipynb)

https://github.com/vishal815/Team-LogicLegends-Proxmed-Hackathon-Hypodense-Segmentation-AI-Project-/assets/83393190/28f5ed50-758d-4e2d-b6e3-24dc2daf77d9

## Team Members

- Vishal Lazrus

- Ritesh Kumar Singh

## Project Overview

The objective of this hackathon challenge is to develop a robust and efficient algorithm or AI model capable of accurately segmenting the hypodense region from Brain Non-Contrast Computed Tomography (NCCT) images. The primary goal is to automate and streamline the identification of early ischemic changes in acute stroke patients.

## Project Structure

The project data has been organized in the following format:

![image](https://github.com/vishal815/Team-LogicLegends-Proxmed-Hackathon-Hypodense-Segmentation-AI-Project-/assets/83393190/8c6c7856-5071-4b3b-99db-7771fa2e05ea)

## Data Preprocessing Steps

1. Extracted zip data and converted it into the specified structure.

2. Preprocessed data to dimensions (128, 128, 128).

3. Performed additional data preprocessing.

## Data Visualization Steps

1. Conducted 2D visualization with different axes.

2. Combined image and label for visualizing hypodense regions.

3. Utilized 3D visualization techniques and HTML5 video visualization.

## Models Implemented

1. **3D U-Net Model (CNN)**

   - Developed a convolutional neural network model for 3D data.

   - Training, testing, and evaluation were carried out.

2. **V-Net CNN Model for 3D Data**

   - Implemented an alternative CNN model for 3D data.

   - Conducted training, testing, and evaluation.

## Additional Steps

- Explored different visualization techniques for better understanding.

- Used Kaggle to host and share data: [Kaggle Data Link](https://www.kaggle.com/datasets/vishallazrus/filtaer-data)

## Usage

Run the main script or notebooks for training and testing.

Install dependencies:

    ```bash

    pip install -r requirements.txt

    ```

## Prerequisites

Ensure you have the following libraries/modules installed:

- TensorFlow

- Seaborn

- Pandas

- html5lib

- NumPy

- Matplotlib

- nibabel

- scikit-image

- os

## Contributions

1. **Vishal Lazrus**

2. **Ritesh Kumar Singh**

## 

The results of this project are good, and Team Proxmed has provided a clear explanation of the problem. Our continuous research, experimentation, and tireless efforts, often extending into the late hours of the night, have been instrumental in this project. We have learned a lot from this project. Thank you 😃.

## Clone/Download

To clone or download this project, you can use the following commands:

```bash

git clone https://github.com/vishal815/Team-LogicLegends-Proxmed-Hackathon-Hypodense-Segmentation-AI-Project-.git