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+# DeepNeuronSeg
+DeepNeuronSeg is a full-stack, end-to-end machine learning pipeline designed for neuroimaging data analysis. This robust framework streamlines the entire workflow, from data preprocessing and augmentation to advanced neural network-based denoising and segmentation. With a focus on performance and ease of use, DeepNeuronSeg empowers researchers to efficiently analyze complex neuroimaging datasets and derive meaningful insights with minimal overhead and lightning fast speeds.
+
+
+# Installation Guide
+
+## Build from conda env (Recommended) 
+
+- Installation requirements
+    - Python
+    - Conda
+    - Git
+- Download the [DeepNeuronSeg.yaml](https://github.com/josh-segal/DeepNeuronSeg/blob/main/DEEPNEURONSEG.yaml) file anywhere on your computer
+- In the terminal, navigate to the folder where the DeepNeuronSeg.yaml file is located
+- run the command `conda env create -f DeepNeuronSeg.yaml` to download DeepNeuronSeg
+- activate the environment with this command `conda activate DEEPNEURONSEG`
+- launch the program with `python -m DeepNeuronSeg`
+- relaunch with `python -m DeepNeuronSeg`
+- if the environment is deactivated, reactivate with `conda activate DEEPNEURONSEG` and launch with `python -m DeepNeuronSeg`
+
+## Build From Source
+
+- Installation requirements
+    - Python
+    - Git
+- In terminal at desired location write commands:
+    - `mkdir test_folder`
+        - makes the desired directory for downloading the project
+    - `cd test_folder`
+        - navigates into the desired directory
+    - `Git clone https://github.com/josh-segal/DeepNeuronSeg.git`
+        - This downloads a copy of the project to your local computer
+    - `cd DeepNeuronSeg`
+        - This navigates into the DeepNeuronSeg project directory
+    - `python -m venv venv`
+        - This creates a python virtual environment to download all the dependencies for DeepNeuronSeg without conflict from your local system/downloads
+    - `venv/Scripts/activate` (Windows) or `source venv/bin/activate` (MacOS)
+        - This activates the virtual environment
+    - `pip install -r requirements.txt`
+        - This installs the dependencies required for DeepNeuronSeg
+    - `python -m DeepNeuronSeg`
+        - This launches the DeepNeuronSeg program, start exploring!
+- To launch again navigate to DeepNeuronSeg directory and re-activate the virtual environment and use `python -m DeepNeuronSeg`
+
+# Usage
+
+## Upload Data
+
+Upload images by selecting png files from file explorer
+
+Upload labels in png (binary mask), csv, txt, XML (last 3 from imageJ cell counter download coordinates)
+
+Option to input project ID, cohort, brain region, image ID
+
+scroll through images to confirm or select through file selector
+
+## Label Data
+
+Display data to load uploaded data
+
+Click on cells in image to set label
+
+Right click to remove cells
+
+Next Image to navigate over data
+
+## Generate Labels
+
+Generate Labels to pass images and labels to label generator
+
+Next image to scroll through data
+
+Display Labels to display on startup
+
+## Create Dataset
+
+Train Split to set amount of data to train on, remainder to validate on
+
+Dataset Name to set name of dataset 
+
+File selector to select which files you want to include in your dataset
+
+## Train Network
+
+Choose base model to train on
+Choose dataset to train with
+
+Set Epochs, batch size for training
+
+Choose trained model name
+
+Choose to train custom denoise model, use default denoise model, or no denoise model
+
+Use default dataset augmentation, no dataset augmentation, or custom dataset augmentation
+
+## Evaluate Network
+
+Choose trained model to evaluate
+
+Choose dataset to evaluate
+
+Calculates average and variability metrics for chosen dataset with chosen model
+
+Optionally display graph of number of detections and confidence of images in dataset
+
+Download data to download a CSV of images to raw metrics
+
+## Analyze Data
+
+Pass through new data to the model and retrieve resultant average and variability metrics
+
+Compares to base dataset and computes a overall variance score to determine if data is outlier
+
+Option to display graph with new data inserted
+
+Option to save inferences as images with predictions marked
+
+## Extract Outliers
+
+Displays data with outlier score above set outlier threshold, user can change threshold manually
+
+User can validate data or relabel data
+
+relabel data inserts image and labels into data, user can add to dataset and retrain
+
+## Model Zoo
+
+User can choose from any of trained models and inference images
+
+Displays inferences for user to inspect
+
+User can save inferences to computer