question_answers_hand_crafted.txt

Question:

How can I open CZI or LIF files using Python?

Answer:

To open CZI or LIF files, you can use the AICSImageIO package. 
In the following code the file `filename` will be loaded and 
the image data will be stored in `image`.

```python
from aicsimageio import AICSImage
aics_image = AICSImage("../../data/EM_C_6_c0.ome.tif")

np_image = aics_image.get_image_data("ZYX")
```

Question:

How can one show an image, its histogram and some basic statistics in a Jupyter notebook?

Answer:

You can show an image stored in the variable `image`, its histogram and some basic statistics using the `stackview` library:

```python
import stackview

stackview.insight(image)
```

Question:

How can I show a label image with every label in a different colour in a Jupyter notebook?

Answer:

You can show an image stored in the variable `image`, its histogram and some basic statistics using the `stackview` library:

```python
import stackview

stackview.insight(image)
```

Question:

How can I show an image in Napari?

Answer:

Napari is a Python based image viewer and you can use it for showing images and label images like demonstrateed in the following:
```python
import napari

# create a viewer
viewer = napari.Viewer()

# show an image in the viewer
viewer.add_image(image)

# show a label image in the viewer
viewer.add_labels(label_image)
```

Question:

How can I remove the background in an image?

Answer:

You can remove the background in an image using scikit-image's `white_tophat` function. 
In the following example the `input_image` will be background-subtracted and 
the result is stored in `output_image`. 
The variable `radius` allows to specify how large objects should stay while removing the background.

```python
from skimage.morphology import disk
from skimage.morphology import white_tophat

radius = 25

output_image = white_tophat(input_image, disk(radius))
```

Question:

How can I remove noise in an image?

Answer:

You can denoise an image using the median filter, e.g. using scikit-image:
In the following example the noise from `input_image` will be removed and 
the result is stored in `output_image`

```python
output_image = filters.median(input_image, disk(1))
```

Question:

How can I segment small roundish objects, such as nuclei or granules, in an image?

Answer:

You can segment and label small roundish objects in an image using the Voronoi-Otsu-Labeling algorithm.
In the following example the objects in `input_image` will be segmented and 
the resulting objects will be labeled and stored in the `label_image`.
The algorithm has two parameters:
* `spot_sigma` allows you to configure how large the objects are supposed to be.
* `outline_sigme` allows you to tune how fine the outlines should be.

In this code, the python library `napari-segment-blobs-and-things-with-membranes` will be used:
```python
import napari_segment_blobs_and_things_with_membranes as nsbatwm

label_image = nsbatwm.voronoi_otsu_labeling(input_image, spot_sigma=2, outline_sigma=2)
```

In case you are using the python library `pyclesperanto-prototype`, you can also call the algorithm like this:

```python
import pyclesperanto_prototype as cle

label_image = cle.voronoi_otsu_labeling(input_image, spot_sigma=2, outline_sigma=2)
```

Question:

How can I segment small roundish objects, such as nuclei, in an image in case they are very densely packed?

Answer:

You can segment densely packed nuclei in an image using the StarDist library.
In the following example the nuclei in the `input_image` will be segmented and
stored in the `label_image`:

```python
from stardist.models import StarDist2D
from csbdeep.utils import normalize

# creates a pretrained model
model = StarDist2D.from_pretrained('2D_versatile_fluo')

# normalize the image
axis_norm = (0,1)
image = normalize(image, 1,99.8, axis=axis_norm)

# segment the image
label_image, details = model.predict_instances(input_image)
```

Question:

How can I segment cells in an image?

Answer:

You can use the CellPose library for that.
In the following example the cells in the `input_image` will be segmented and
stored in the `label_image`:

```python
from cellpose import models, io

model = models.Cellpose(gpu=False, model_type='cyto')

channels = [0,0] # This means we are processing single-channel greyscale images.

label_image, flows, styles, diams = model.eval(input_image, diameter=None, channels=channels)
```

Question:

How can I re-label labels sequentially in a label image?

Answer:

You can re-label labels in a label image using scikit-image's `relabel_sequential` function like this:

```python
from skimage.segmentation import relabel_sequential

relabeled_image, _, _ = relabel_sequential(label_image)
```

Question:

How can I expand labels e.g. with a radius of 10 pixels in a label image?

Answer:

You can expand labels in a label image using scikit-image's `expand_labels` function like this:

```python
from skimage.segmentation import expand_labels

expanded_labels = expand_labels(filtered_label_image, distance=10)
```