Mask manager module

This module is responsible for creating and applying the masks. The idea and lots of the code were very heavily borrowed from this study: https://www.cis.upenn.edu/~jshi/ped_html/

class library.image_manipulation.mask_manager.MaskManager

Bases: object

Class containing all methods related to image masks This class is used to create masks for each tiff image (post-extraction from czi files), apply user-modified masks

Note: Uses pytorch for ML generation of masks

apply_user_mask_edits()

Apply the edits made on the image masks to extract the tissue from the surround debris to create the final masks used to clean the images

create_downsampled_mask(channel=1)

Create masks for the downsampled images using a machine learning algorithm. The input files are the files that have been normalized.

create_full_resolution_mask(channel=1)

Upsample the masks created for the downsampled images to the full resolution

create_mask()

Helper method to call either full resolition of downsampled. Create the images masks for extracting the tissue from the surrounding debris using a CNN based machine learning algorithm

get_model_instance_segmentation(num_classes)

This loads the mask model CNN

Parameters:

num_classes – int showing how many classes, usually 2, brain tissue, not brain tissue

load_machine_learning_model()

Load the CNN model used to generate image masks

static resize_tif(file_key)

Function to upsample mask images

Parameters:

file_key – tuple of inputs to the upsampling program including:

• path to thumbnail file

• The output directory of upsampled image

• resulting size after upsampling