hcat 0.2.17

A Hair Cell Analysis Toolbox

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
• In a terminal or anaconda prompt, copy and paste: `conda create -yn hcat python=3.9'
• Then copy and paste: conda activate hcat
• Install pytorch for CPU ONLY: conda install pytorch torchvision torchaudio cpuonly -c pytorch
• Install hcat dependencies: pip install hcat
• Run hcat: conda activate 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

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.

1. 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.
2. On Windows, launch the Anaconda Prompt application. On Mac or Linux launch a new terminal. If installed correctly you should see (base) to the left of your terminal input. . This is your anaconda environemnt.
3. 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, type conda create -n hcat python=3.9 and type y when asked. This creates an environment to install our software. We must now activate this environment to access our isolated sandbox and install hcat.
4. To activate our environment, type in the terminal conda activate hcat. Notice how (base) has been replaced with (hcat).
5. 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

NOTE Installing pytorch with pip is known to cause issues in some systems. To ensure GPU capabilities and prevent errors, please install with the package manager Conda.

6. Once we've installed pytorch, we can use the command line to install hcat. To do this, type pip install hcat. This command will install all remaining libraries necessary to run the software into our protected sandbox environment (this may take a while). This means that the software can only be called from the hcat environment.

7. If the installation finishes with no errors, we can run hcat by simply typing hcat in the prompt!

NOTE: If you restart your prompt or terminal, you may need to reactivate the environment to launch the program. To activate and launch hcat in a single line, copy and paste into the prompt: conda activate hcat; hcat

---

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

1. 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 for hcat segment. For hcat detect check that the order of your channels is correct (cytosol then hair bundle).
2. 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 the Image dropdown then clicking type, it should show either 8-bit or 16-bit.
3. 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')