A Hair Cell Analysis Toolbox
Project description
HCAT - Hair Cell Analysis Toolbox
Hcat is a suite of machine learning enabled algorithms for performing common image analyses in the hearing field. At present, it performs two fully automated analyses: (1) 2D hair cell detection (2) Volumetric hair cell segmentation.
This tool is capable of automatically generating cochleograms, determine cell fluorescent intensity statistics, and investigating tonotopic and morphological trends.
Quickstart Guide
Installation
- Install Anaconda
- Perform the installation by copying and pasting the following comands into your Anaconda Prompt (Windows) or Terminal (Max/Linux)
- Create a new anaconda environment:
conda create -yn hcat python=3.9
- Activate the anaconda environment:
conda activate hcat
WARNING: You will need to avtivate your conda environment every time you restart your prompt!
- Install pytorch for CPU ONLY:
conda install pytorch torchvision torchaudio cpuonly -c pytorch
- Install hcat and dependencies:
pip install hcat --upgrade
- Run hcat:
hcat
NOTE: It is strongly recommended you follow the installation guide for correct installation!
NOTE: Follow the detailed installation guide for instructions on how to enable GPU acceleration
Detection Gui:
- Run in terminal:
hcat
CLI Hair Cell Detection Analysis:
- Run in terminal:
hcat detect "path/to/file.tif"
CLI Segmentation Analysis (beta):
- Run in terminal:
hcat segment "path/to/file.tif"
Requirements
The following requirements are necessary to run hcat
.
- Pytorch 1.12.0
- Torchvision 0.13.0
- python 3.9
Detailed Installation
To install hcat, ensure you that Python Version 3.9 as well as all dependencies properly installed. It is recommended to use the Anaconda distribution of python with a dedicated environment. To do a reccomendned install, please use the following steps.
- Download the Anaconda distribution of python from the following link: Anaconda. This will install python for you! There is no need to install python from an additional source.
- On Windows, launch the
Anaconda Prompt
application. On Mac or Linux launch a newterminal
. If installed correctly you should see(base)
to the left of your terminal input. This is your anacondaenvironemnt
. - To avoid dependency issues, we will create a new environment to install hcat. This acts like an isolated sandbox where
we can install specific
versions
necessary software. To do this, in the prompt, typeconda create -n hcat python=3.9
and typey
when asked. This creates an environment to install our software. We must now activate this environment to access our isolated sandbox and installhcat
. - To activate our environment, type in the terminal
conda activate hcat
. Notice how(base)
has been replaced with(hcat)
. - To run hcat we first need to install
pytorch
, a deep learning library. To do this, follow the instructions on the Pytorch website for your particular system. It is recommended to use these install settings:
Setting | Selection |
---|---|
PyTorch Build | Stable (1.12.0) |
Your OS | Linux/Mac/Windows |
Package | Conda |
Language | Python |
Compute Platform | CUDA 11.3 (If you have an Nvidia GPU, otherwise use CPU) |
This will create a command to run in the prompt. With these settings, this might look like: conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
.
This may take a while.
NOTE: Installing pytorch with pip can cause issues in some systems. To ensure GPU capabilities and prevent errors, please install with the package manager
Conda
.
-
Once we've installed pytorch, we can use the command line to install
hcat
. To do this, typepip install hcat --upgrade
. This command will install all remaining libraries necessary to run the software into our protected sandbox environment. This means that the software can only be called from the hcat environment. -
If the installation finishes with no errors, we can run
hcat
by simply typinghcat
in the prompt!
WARNING: If you restart your prompt or terminal, you will need to reactivate the environment to launch the program.
Detection Gui
We provide an easy to use gui to the performance of hcat's detection capabilites on your own data. To launch the gui and analyze your own data, please follow the following steps.
- Open a terminal (Mac/Linux) or Anaconda Prompt (Windows) and type:
conda activate hcat
- Verify the environment is activated by
(hcat)
being displayed to the left of the input prompt - Type
hcat
in the prompt to launch the gui A gui should launch. - On the top left, click browse to select a file, then load to load the file into the software. The image should be displayed on the screen.
- Enter the diameter of the cell in pixels. Best measured in ImageJ. Click 'OK'
- De-select the channels not representing the cell cytosol and hair bundle.
- Adjust the brightness and contrast of the image to minimize background fluorescence.
- Press 'Run Analysis'. A pretrained model will download off the internet and loaded into the software. This may take a few minutes and should only happen once.
- Predictions should appear on screen. Fine tune the predictions by adjusting the cell probability rejection threshold and prediction box overlap threshold.
- Press 'Save' to save the analysis to a CSV and JPG. This will create two new files in the same location as your image called:
<filename>_analysis.csv
and<filename>_analysis.jpg
Comand Line Usage
The comand line tool has two entry points: detect, and segment (Beta).
- detect takes in a 2D, multichannel maximum projection of a cochlea and predicts inner and outer hair cell detection predictions
- segment (Beta) takes in a 3D, multichannel volume of cochlear hair cells and generates unique segmentation masks which which may be used
Detect
hcat detect
is the entrypoint for the detection of hair cells from max projection tilescans of a cochlea.
Hair cell detection is one of the most basic tasks in cochlear image analysis;
useful for evaluating cochlear trauma, aging, ototoxicity, and noise exposure. To evaluate an image, run the following in
the command line:
hcat detect [INPUT] [OPTIONS]
INPUT
The program accepts confocal max-projected z-stacks of cochlear hair cells stained with a hair cell specific cytosol stain (usually anti-Myo7a) and a stereocilia stain (ESPN, phalloidin, etc...). The input image must only have these 2 channels. This may be easiest achieved with the Fiji application. The best performing images will have high signal-to-noise ratio and low background staining.
OPTIONS
--cell_detection_threshold (float) Rejection for objects with mean cytosolic intensity below threshold
--curve_path (str) Path to collection of points for curve estimation
--dtype (str) Data type of input image: (uint8 or uint16)
--save_fig (flag) Render diagnostic figure containing cell detection information
--save_xml (flag) Save detections as xml format compatable with labelImg software
--pixel_size (int) X/Y pixel size in nm
--cell_diameter (int) Rough diameter of hair cell in pixels
OUTPUT
The program will save two files with the same name and in the same location as the original file: filename.csv
and
filename.cochlea
.
filename.csv
contains human-readable data on each hair cell segmented in the original image.filename.cochlea
is a dataclass of the analysis which is accessible via the python programing language and contains a compressed tensor array of the predicted segmentation mask.
To access filename.cochela
in a python script:
import torch
from hcat.lib.cell import Cell
from typing import List
# Detected cells are stored as "Cell" objects
cochlea = torch.load('filename.cochlea')
cells: List[Cell] = cochlea.cells
# To access each cell:
for cell in cells:
print(cell.loc, cell.frequency) #location (x, y, z); frequency (Hz)
Common Issues
- The program doesn't predict anything: This is most likely a channel issue. The machine learning backbones to each
model is not only channel specific, but also relies on specific channel ordering. Check the
--channel
flag is set properly forhcat segment
. Forhcat detect
check that the order of your channels is correct (cytosol then hair bundle). - The program still doesn't show anything: If it is not the channel, then it is likely a datatype issue. Ensure you are
passing in an image of dtype uint8 or uint16. This can be double checked in the
fiji
application by clicking theImage
dropdown then clickingtype
, it should show either 8-bit or 16-bit. - I cannot find the output: The program saves the output of each analysis as a CSV file with the same name in the same location as the original file! Beware, subsequent excecutions of this program will overwrite previous analysis files.
Segment (Beta)
hcat segment
is the entrypoint for volumetric segmentation of hair cells. The program will iteratively segment arbitrarily
sized images, up to every hair cell in a cochlea. This is particularly useful for gene therapy studies, where nuanced measures
of cell intensity may provide insight in the efficacy of a gene therapy construct. To evaluate an image, run the following in the
command line:
hcat segment [INPUT] [OPTIONS]
INPUT
The program accepts volumetric confocal images of cochlea in the tiff format. The cells must be stained with a hair cell specific cytosol stain (usually anti-Myo7a), at either 8-bit or 16-bit resolution. The best performing images are one with high signal-to-noise ratio, low background staining, and good separation between hair cells. Confocal z-stacks postprocessed with deconvolution algorithms may only aid in segmentation accuracy.
OPTIONS
--channel (int) cytosolic channel index, (0 for greyscale)
--intesnity_reject_threshold (float) Rejection for objects with mean cytosolic intensity below threshold
--dtype (str) Data type of input image: (uint8 or uint16)
--unet (flag) Generate segmentation masks with U Net + Watershed backbone
--cellpose (flag) Generate segmentation masks with 3D cellpose backbone
--figure (flag) Save a preliminary analysis figure
--no_post (experimental, flag) Disable postprocessing on detection
OUTPUT
The program will save two files with the same name and in the same location as the original file: filename.csv
and
filename.cochlea
.
filename.csv
contains human-readable data on each hair cell segmented in the original image.filename.cochlea
is a dataclass of the analysis which is accessible via the python programing language and contains a compressed tensor array of the predicted segmentation mask.
To access filename.cochela
in a python script:
import torch
cochlea = torch.load('filename.cochlea')
# If the mask is compressed
cochlea.decompress_mask()
predicted_segmentation_mask = cochlea.mask
print('Mask Shape: ', predicted_segmentation_mask.shape)
# Mask Shape: torch.Shape[5000,6000,35]
Alternativley you may access the cochlea object via the hcat package:
from hcat.lib.cochlea import Cochlea
cochela = Cochlea.load('filename.cochlea')
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