From c0af68636e11c87fc39be65d38a45e3c548b44ea Mon Sep 17 00:00:00 2001 From: alessandrofelder Date: Mon, 30 Sep 2024 13:44:24 +0100 Subject: [PATCH 1/3] add section for intro to image analysis --- img/blank-napari-ui.png | Bin 0 -> 30428 bytes img/cells-napari-pan-zoom.png | Bin 0 -> 75163 bytes img/cells-napari.png | Bin 0 -> 169506 bytes img/coordinate-system.png | Bin 0 -> 197957 bytes img/coordinates-on-image.png | Bin 0 -> 66659 bytes img/coordinates.png | Bin 0 -> 68767 bytes img/dim-slider-0.png | Bin 0 -> 165814 bytes img/dim-slider-100.png | Bin 0 -> 171280 bytes img/dim-slider-50.png | Bin 0 -> 171754 bytes img/dim-slider-closeup.png | Bin 0 -> 52286 bytes img/dim-slider.png | Bin 0 -> 430500 bytes img/filtered.png | Bin 0 -> 117426 bytes img/labelled.png | Bin 0 -> 72788 bytes img/napari-console.png | Bin 0 -> 187983 bytes img/pixel-value.png | Bin 0 -> 69478 bytes img/regionprops.png | Bin 0 -> 98156 bytes img/thresholded.png | Bin 0 -> 77018 bytes img/ui-sections-napari.png | Bin 0 -> 361446 bytes index.qmd | 9 ++ slides/intro-napari.qmd | 213 ++++++++++++++++++++++++++++++++++ slides/processing-images.qmd | 53 +++++++++ slides/what-is-an-image.qmd | 211 +++++++++++++++++++++++++++++++++ 22 files changed, 486 insertions(+) create mode 100644 img/blank-napari-ui.png create mode 100644 img/cells-napari-pan-zoom.png create mode 100644 img/cells-napari.png create mode 100644 img/coordinate-system.png create mode 100644 img/coordinates-on-image.png create mode 100644 img/coordinates.png create mode 100644 img/dim-slider-0.png create mode 100644 img/dim-slider-100.png create mode 100644 img/dim-slider-50.png create mode 100644 img/dim-slider-closeup.png create mode 100644 img/dim-slider.png create mode 100644 img/filtered.png create mode 100644 img/labelled.png create mode 100644 img/napari-console.png create mode 100644 img/pixel-value.png create mode 100644 img/regionprops.png create mode 100644 img/thresholded.png create mode 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+42,7 @@ format: ## Schedule{.smaller} ::: {.incremental} - Installing BrainGlobe (30 mins) +- Introduction to Image analysis with Napari - Introduction to BrainGlobe (30 mins) - Registering whole brain microscopy images with brainreg (30 mins) - Segmenting structures in whole brain microscopy images with brainglobe-segmentation (30 mins) @@ -56,6 +57,14 @@ format: {{< include slides/installation.qmd >}} +# Image Analysis +Adapted from [https://github.com/HealthBioscienceIDEAS/microscopy-novice/](https://github.com/HealthBioscienceIDEAS/microscopy-novice/) (under CC BY 4.0 license) + +{{< include slides/intro-napari.qmd >}} +{{< include slides/what-is-an-image.qmd >}} +{{< include slides/processing-images.qmd >}} + + # BrainGlobe {{< include slides/brainglobe-intro.qmd >}} diff --git a/slides/intro-napari.qmd b/slides/intro-napari.qmd new file mode 100644 index 0000000..2ac902a --- /dev/null +++ b/slides/intro-napari.qmd @@ -0,0 +1,213 @@ +## Opening Napari + +```{.bash} +napari +``` + +![](img/blank-napari-ui.png){alt="A screenshot of the default Napari user +interface"} + +## Opening images + +`File > Open Files(s)`, then navigate to `calcium imaging folder` and open `translation1_00001_ce.tif` + +![](img/cells-napari.png){alt="A screenshot of a some fluorescent cells"} + +## Napari's User interface + + +![](img/ui-sections-napari.png){alt="A screenshot of Napari with the main user +interface sections labelled"} + +## Main menu + +![](img/ui-sections-napari.png){alt="A screenshot of Napari with the main user +interface sections labelled"} + +::: {.notes} +We already used the main menu in the last section to view the BrainGlobe plugins. The +main menu contains various commands for opening images, changing preferences and +installing plugins (we'll see more of these options in later episodes). +::: + +## Canvas + +Try moving around the image with the following commands: +``` +Pan - Click and drag +Zoom - Scroll in/out +``` + +![](img/cells-napari-pan-zoom.png){alt="A screenshot of a some fluorescent cells, closer up than before."} + +## Dimension sliders + + +![](img/dim-slider-closeup.png){alt="Closeup of Napari's dimension slider with +labels" width='80%'} + +![](img/dim-slider-0.png){width="30%" alt="Three screenshots of the cells image in napari, at +different z depths"} +![](img/dim-slider-50.png){width="30%" alt="Three screenshots of the cells image in napari, at +different z depths"} +![](img/dim-slider-100.png){width="30%" alt="Three screenshots of the cells image in napari, at +different z depths"} + +::: {.notes} +Dimension sliders appear at the bottom of the canvas depending on the type of +image displayed. For example, here we have a 3D image of some cells, which +consists of a stack of 2D images. If we drag the slider at the bottom of the +image, we move up and down in this stack: + +Pressing the arrow buttons at either end of the slider steps through one slice +at a time. Also, pressing the 'play' button at the very left of the slider moves +automatically through the stack until pressed again. + +We will see in later episodes that more sliders can appear if our image has more +dimensions (e.g. time series, or further channels). +::: + +## Viewer buttons {.smaller} + +The viewer buttons (the row of buttons at the bottom left of Napari) control +various aspects of the Napari viewer: + +:::: {.columns} + +::: {.column width=50%} + - Console ![]( +https://raw.githubusercontent.com/napari/napari/main/napari/resources/icons/console.svg +){alt="A screenshot of Napari's console button" height='30px'} + + - 2D/3D ![]( +https://raw.githubusercontent.com/napari/napari/main/napari/resources/icons/2D.svg +){alt="A screenshot of Napari's 2D button" height='25px'} / ![]( +https://raw.githubusercontent.com/napari/napari/main/napari/resources/icons/3D.svg +){alt="A screenshot of Napari's 3D button" height='25px'} + + - Roll dimensions ![]( +https://raw.githubusercontent.com/napari/napari/main/napari/resources/icons/roll.svg +){alt="A screenshot of Napari's roll dimensions button" height='25px'} + + - Transpose dimensions ![]( +https://raw.githubusercontent.com/napari/napari/main/napari/resources/icons/transpose.svg +){alt="A screenshot of Napari's transpose dimensions button" height='25px'} + + - Grid ![]( +https://raw.githubusercontent.com/napari/napari/main/napari/resources/icons/grid.svg +){alt="A screenshot of Napari's grid button" height='25px'} + + - Home ![]( +https://raw.githubusercontent.com/napari/napari/main/napari/resources/icons/home.svg +){alt="A screenshot of Napari's home button" height='25px'} +::: + +::: {.column width=50%} +![](img/cells-napari.png){alt="A screenshot of a some fluorescent cells"} +::: +:::: + +## Layer list + +![](img/cells-napari.png){alt="A screenshot of a some fluorescent cells"} + +::: {.notes} +We can show/hide each layer by clicking the eye icon on the left side of their +row. +We can also rename them by double clicking on the row. + +We can change the order of layers by dragging and dropping items in the layer +list. For example, try dragging the membrane layer above the nuclei. You should +see the nuclei disappear from the viewer (as they are now hidden by the membrane +image on top). + +Now that we've seen the main controls for the viewer, let's look at the layer +list. 'Layers' are how Napari displays multiple items together in the viewer. +For example, currently our layer list contains two items - 'nuclei' and +'membrane'. These are both `Image` layers and are displayed in order, with +the nuclei on top and membrane underneath. + +Here we only have `Image` layers, but there are many more types like `Points`, +`Shapes` and `Labels`, some of which we will see [later in the episode +](#layer-buttons). +::: + +## Layer controls + +This area shows controls only for the currently selected layer (i.e. the one that is highlighted in blue in the layer +list). + +:::: {.columns} +::: {.column width=30%} + - Opacity + - Contrast limits + - ... +::: + +::: {.column width=60%} +![](img/cells-napari.png){alt="A screenshot of a some fluorescent cells"} +::: +:::: + +::: {.notes} + +This changes the opacity of the layer - lower values are more transparent. For +example, reducing the opacity of the membrane layer (if it is still on top of +the nuclei), allows us to see the nuclei again. + +briefly - the contrast +limits adjust what parts of the image we can see and how bright they appear in +the viewer. Moving the left node adjusts what is shown as fully black, while +moving the right node adjusts what is shown as fully bright. This doesn't change the values +in the image, just how they are displayed. +::: + +## Layer buttons + +:::: {.columns} + +::: {.column width=40%} +Create and remove. + + - Points ![]( +https://raw.githubusercontent.com/napari/napari/main/napari/resources/icons/new_points.svg +){alt="A screenshot of Napari's point layer button" height='30px'} + + - Shapes ![]( +https://raw.githubusercontent.com/napari/napari/main/napari/resources/icons/new_shapes.svg +){alt="A screenshot of Napari's shape layer button" height='30px'} + + - Labels ![]( +https://raw.githubusercontent.com/napari/napari/main/napari/resources/icons/new_labels.svg +){alt="A screenshot of Napari's labels layer button" height='30px'} + + - Remove layer ![]( +https://raw.githubusercontent.com/napari/napari/main/napari/resources/icons/delete.svg +){alt="A screenshot of Napari's delete layer button" height='30px'} +::: + + +::: {.column width=55%} +![](img/cells-napari.png){alt="A screenshot of a some fluorescent cells"} +::: + +:::: + +## Other layer types + +Note that there are some layer types that can't be added via clicking buttons +in the user interface, like + +* [surfaces](https://napari.org/stable/howtos/layers/surface.html), +* [tracks](https://napari.org/stable/howtos/layers/tracks.html) and +* [vectors](https://napari.org/stable/howtos/layers/vectors.html). + +These require +calling python commands in Napari's console or an external python script. + +## Key points + +- Napari's user interface is split into a few main sections including the +canvas, layer list, layer controls... +- Layers can be of different types e.g. `Image`, `Point`, `Label` +- Different layer types have different layer controls diff --git a/slides/processing-images.qmd b/slides/processing-images.qmd new file mode 100644 index 0000000..b0f8b2e --- /dev/null +++ b/slides/processing-images.qmd @@ -0,0 +1,53 @@ +## Processing images + +Now we understand what an image is, and how to look at it in napari, we can start measuring things! +But we need to find ("segment") "things" first! + +![](img/cells-napari.png){alt="A screenshot of a some fluorescent cells"} + +## Reduce noise with a median filter + +``` {.python} +from scipy.signal import medfilt2d +image = viewer.layers[0].data +filtered = medfilt2d(image) +viewer.add_image(filtered) +``` + +![](img/filtered.png){alt="A median-filtered version of the example image"} + +## Isolate neurons with a threshold {.smaller} + +Example of "semantic" segmentation +``` {.python} +thresholded = filtered > 8000 # True or False array +thresholded = thresholded*255 # convert to unsigned int 8-bit +viewer.add_image(thresholded) +``` +![](img/thresholded.png){alt="A thresholded version of the example image"} + +## Label each neuron with a number {.smaller} + +Example of "instance" segmentation +``` {.python} +from skimage.measure import regionprops, label +labelled = label(thresholded) +viewer.add_labels(labelled) +``` +![](img/labelled.png){alt="A labelled version of the example image"} + +## Pixels in each neuron {.smaller} + +``` {.python} +properties = regionprops(labelled) +pixels_in_each_region = [prop.area for prop in properties] +print(pixels_in_each_region) +``` +![](img/regionprops.png){alt="A list of pixel counts for each region of the example image"} + +## Key points + +* Segmentation can be broadly split into ‘semantic segmentation’ and ‘instance segmentation’. Semantic segmentation assigns pixels to specific classes (like neuron or background), while instance segmentation assigns pixels to individual ‘instances’ of a class (like individual neurons). +* Segmentations are represented in the computer in the same way as standard images. The difference is that pixel values represent classes or instances, rather than light intensity. +* Napari uses Labels layers for segmentations. +* Segmentation is helpful for analysis. \ No newline at end of file diff --git a/slides/what-is-an-image.qmd b/slides/what-is-an-image.qmd new file mode 100644 index 0000000..30e0e2d --- /dev/null +++ b/slides/what-is-an-image.qmd @@ -0,0 +1,211 @@ +## A deeper look + +`File > Open Files(s)`, then navigate to `calcium imaging folder` and open `translation1_00001_ce.tif` + +![](img/cells-napari.png){alt="A screenshot of a some fluorescent cells"} + +## Pixels + +::: {.notes} +This 2D image shows the nuclei of human cells undergoing mitosis. If we really +zoom in up-close by scrolling, we can see that this image is actually made up of +many small squares with different brightness values. These squares are the +image's pixels (or 'picture elements') and are the individual units that make +up all digital images. + +If we hover over these pixels with the mouse cursor, we can see that each pixel +has a specific value. Try hovering over pixels in dark and bright areas of the +image and see how the value changes in the bottom left of the viewer: +::: + +![](img/pixel-value.png){alt="A screenshot of Napari - with the mouse cursor +hovering over a pixel and highlighting the corresponding pixel value"} + + +## Images are arrays of numbers {.smaller} + +:::: {.columns} + +::: {.column width=45%} +Open Napari's built-in console ![]( +https://raw.githubusercontent.com/napari/napari/main/napari/resources/icons/console.svg +){alt="A screenshot of Napari's console button" height='30px'} and run: + +``` {.python} +# Get the image data for the first layer in Napari +image = viewer.layers[0].data + +# Print the image values and type +print(image) +print(type(image)) +``` +::: + +::: {.column width=55%} +![](img/napari-console.png){alt="A screenshot of Napari's console"} +::: + +:::: + +::: {.notes} +We've seen that images are made of individual units called pixels that have +specific values - but how is an image really represented in the computer? Let's +dig deeper into Napari's `Image` layers... + +All of the information about the Napari viewer can be accessed through the +console with a variable called `viewer`. A `viewer` has 1 to many layers, and +here we access the top (first) layer with `viewer.layers[0]`. Then, to access +the actual image data stored in that layer, we retrieve it with `.data`: + +You should see that a series of numbers are printed out that are stored in a +Python data type called a `numpy.ndarray`. Fundamentally, this means that all +images are really just arrays of numbers (one number per pixel). Arrays are just +rectangular grids of numbers, much like a spreadsheet. Napari is reading those +values and converting them into squares of particular colours for us to see in +the viewer, but this is only to help us interpret the image contents - the +numbers are the real underlying data. + +In Napari this array is a `numpy.ndarray`. [NumPy](https://numpy.org/) is a +popular python package that provides 'n-dimensional arrays' (or 'ndarray' for +short). N-dimensional just means they can support any number of dimensions - +for example, 2D (squares/rectangles of numbers), 3D (cubes/cuboids of numbers) +and beyond (like time series, images with many channels etc. where we would +have multiple rectangles or cuboids of data which provide further information +all at the same location). +::: + +## Image dimensions + +:::: {.columns} + +::: {.column width=45%} +We can find out the dimensions by running the following in Napari's console: +``` {.python} +image.shape +``` +::: + +::: {.column width=55%} +![](img/napari-console.png){alt="A screenshot of Napari's console"} +::: + +:::: + + +::: {.notes} +Let's return to our image and explore some of the key features of +its image array. First, what size is it? + +The array size (also known as its dimensions) is stored in the `.shape`. Three values are printed as this image is three dimensional (3D), for a 2D image there would be 2, for a 4D image there would be 4 and so on... +::: + +## Image data type + +:::: {.columns} + +::: {.column width=45%} +The other key feature of an image array is its 'data type' - this controls which +values can be stored inside of it. +``` {.python} + +image.dtype.name +``` +The data type consists of type and bit-depth. +::: + +::: {.column width=55%} +![](img/napari-console.png){alt="A screenshot of Napari's console"} +::: + +:::: + + +::: {.notes} +For example, let's look at the data type for +our image - this is stored in `.dtype`: + +We see that the data type (or 'dtype' for short) is `uint16`. This is short for +'unsigned integer 16-bit'. Let's break this down further into two parts - the +type (unsigned integer) and the bit-depth (16-bit). +::: + +## Type + +The type determines what kind of values can be stored in the array, for example: + +- Unsigned integer: positive whole numbers +- Signed integer: positive and negative whole numbers +- Float: positive and negative numbers with a decimal point e.g. 3.14 + +::: {.notes} +For our calcium image, 'unsigned integer' means that only positive whole numbers +can be stored inside. You can see this by hovering over the pixels in the image +again in Napari - the pixel value down in the bottom left is always a positive +whole number. +::: + +## Bit depth + +The bit depth determines the range of values that can be stored e.g. only values +between 0 and $2^{16}-1$. + +``` {.python} +print(image.min()) +print(image.max()) +``` + + + +## Common data types {.smaller} + +NumPy supports a [very wide range of data types]( +https://numpy.org/doc/stable/reference/arrays.scalars.html#sized-aliases), +but there are a few that are most common for image data: + +| NumPy datatype | Full name | Range of values +| :--------- | :-------------- | :-------------- +| `uint8` | Unsigned integer 8-bit | 0...255 +| `uint16` | Unsigned integer 16-bit | 0...65535 +| `float32` | Float 32-bit | $-3.4 \times 10^{38}...+3.4 \times 10^{38}$ +| `float64` | Float 64-bit | $-1.7 \times 10^{308}...+1.7 \times 10^{308}$ + +`uint8` and `uint16` are most common for images from light microscopes. +`float32` and `float64` are common during image processing (as we will see in +later episodes). + +## Coordinate system {.smaller} + +![](img/coordinate-system.png){alt="Diagram comparing a standard graph +coordinate system (left) and the image coordinate system (right)" width=60%} +![](img/coordinates-on-image.png +){alt="A diagram showing how pixel coordinates change over a simple 4x4 image" width=30%} + +* For 2d images, `y` is the first coordinate +* For 3d images, `z` is the first coordinate + +::: {.notes} +We've seen that images are arrays of numbers with a specific shape (dimensions) +and data type. How do we access specific values from this array? What coordinate +system is Napari using? + +As you move around, you should see that the lowest coordinate values are at the +top left corner, with the first value increasing as you move down and the second +value increasing as you move to the right. This is different to the standard +coordinate systems you may be used to (for example, from making graphs): + +Note that Napari lists coordinates as [y, x] or [rows, columns], so e.g. [1,3] +would be the pixel in row 1 and column 3. Remember that these coordinates always +start from 0 as you can see in the diagram: +::: + + +## Key points + +- Digital images are made of pixels +- Digital images store these pixels as arrays of numbers +- Napari (and Python more widely) use NumPy arrays to store images - +these have a `shape` and `dtype` +- Most images are 8-bit or 16-bit unsigned integer +- Images use a coordinate system with (0,0) at the top left, x increasing to +the right, and y increasing down + From 1a0f356b154e22b6f4c4e052668ac8ef9f73c604 Mon Sep 17 00:00:00 2001 From: Alessandro Felder Date: Mon, 30 Sep 2024 14:17:07 +0100 Subject: [PATCH 2/3] Update slides/what-is-an-image.qmd Co-authored-by: Adam Tyson --- slides/what-is-an-image.qmd | 1 - 1 file changed, 1 deletion(-) diff --git a/slides/what-is-an-image.qmd b/slides/what-is-an-image.qmd index 30e0e2d..2028f87 100644 --- a/slides/what-is-an-image.qmd +++ b/slides/what-is-an-image.qmd @@ -107,7 +107,6 @@ The array size (also known as its dimensions) is stored in the `.shape`. Three v The other key feature of an image array is its 'data type' - this controls which values can be stored inside of it. ``` {.python} - image.dtype.name ``` The data type consists of type and bit-depth. From 1a5c24fad8478b633e70095fe21815cd62aa6739 Mon Sep 17 00:00:00 2001 From: alessandrofelder Date: Mon, 30 Sep 2024 14:25:59 +0100 Subject: [PATCH 3/3] shorten summary slide --- slides/processing-images.qmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/slides/processing-images.qmd b/slides/processing-images.qmd index b0f8b2e..771c70f 100644 --- a/slides/processing-images.qmd +++ b/slides/processing-images.qmd @@ -47,7 +47,7 @@ print(pixels_in_each_region) ## Key points -* Segmentation can be broadly split into ‘semantic segmentation’ and ‘instance segmentation’. Semantic segmentation assigns pixels to specific classes (like neuron or background), while instance segmentation assigns pixels to individual ‘instances’ of a class (like individual neurons). -* Segmentations are represented in the computer in the same way as standard images. The difference is that pixel values represent classes or instances, rather than light intensity. -* Napari uses Labels layers for segmentations. +* Segmentation can be broadly split into ‘semantic segmentation’ (e.g. neuron vs background) and ‘instance segmentation’ (e.g. individual neuron). +* Segmentations are represented in the computer in the same way as images, but pixels represent an abstraction, rather than light intensity. +* Napari uses "Labels" layers for segmentations. * Segmentation is helpful for analysis. \ No newline at end of file

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