mnfinder 1.2.2

A package for segmenting micronuclei in microscopy images

MN UNet segmenter

A package for segmenting micronuclei in micrographs.

Quick-start

from mnfinder import MNClassifier
import numpy as np
from tifffile import TiffFile

trained_model = MNClassifier.get_model()

image = TiffFile.imread('path/to/image.tiff').asarray()
labels = trained_model.predict(image)

Installation

MNFinder depends on TensorFlow. It will be installed for you via pip.

pip

pip install mnfinder

Usage

Loading a model

trained_model = MNModel.get_model([model_name])

MNFinder supports several different trained models with different architectures. The default is an Attention U-Net.

Weights will be automatically downloaded.

Available models

Attention

The default network is an Attention U-Net that was trained on 128x128 crops.

Defaults

• skip_opening: False
• expand_masks: True
• use_argmax: True
• opening_radius: 1

MSAttention

This is a modification of the Attention U-Net that incorporates a multi-scale convolution in the down blocks.

Defaults

• skip_opening: False
• expand_masks: True
• use_argmax: True
• opening_radius: 1

Combined

An Attention U-Net trained on the micronucleus output of Attention and MSAttention.

Defaults

• skip_opening: False
• expand_masks: True
• use_argmax: True
• opening_radius: 1

LaplaceDeconstruction

Images are first transformed into Laplace pyramids, and recombined only using the top 2 levels of the pyramid to highlight cell edges.

Defaults

• skip_opening: False
• expand_masks: True
• use_argmax: False
• opening_radius: 2

Predictions

img = np.array(Image.open("my/image.png"))
labels = trained_model.predict(img, skip_opening=[bool], expand_masks=[bool], use_argmax=[bool], area_thresh=[int], return_raw_output=[bool])

A single method is used to predict and label nuclear and micronucler segments.

These neural nets were trained on images taken at 20x. Predictions for micrographs taken at other resolutions are greatly improved if they are scaled to match a 20x resolution.

Images of arbitrary size will be cropped by a sliding window and segments combined.

Labels are returned as a 3-channel image. Channel 1 contains the unique cell label for each segmented nucleus; channel 2 has each micronucleus labelled with its corresponding cell label; channel 3 has a unique label for each micronucleus.

Optional parameters

skip_opening=bool : Whether to skip running opening on MN predictions prior to labelling. Many models are improved by removing small 1- or 2-px segments by image opening—erosion following by dilation. Defaults to the model default.

expand_masks=bool : Whether to expand micronucleus masks by returning the convex hulls of each segment. Defaults to the model default.

use_argmax=bool : Whether to determine pixel classes by taking the maximum probability. Some models are improved by instead setting a simple threshold on the micronucleus class probability, setting a pixel to the micronucleus class even if the model’s nucleus class probability is higher. If use_argmax is False, the model will select pixels with a background class > model.bg_max and a micronucleus class < model.fg_min. Defaults to the model default.

area_thresh=int|False : Large micronuclei separated from the nucleus are often classed as nuclei. Any nucleus segments < area_thresh will be converted to micronuclei. Set this to False to skip this conversion. Defaults to 250.

return_raw_output=bool : If you wish to examine the fields returned by the semantic and instance classifier neural nets, set this to True.

Prediction info

mn_df, nuc_df = MNClassifier.get_label_data(labels)

Provides some basic information about each MN and nuclear label predicted.