Image annotation with ScanImage in MATLAB

Using two-photon microscopy, we can observe microscopic structures such as dendritic spines in the living brain of mice. We can then follow them over time and observe their dynamics, such as changes in their numbers, their size, or their positions. For my Ph.D. project, I used the annotation module of ScanImage to track dendritic spines over time.

Annotation and photometry modules

In the example above, the tool is focused on a spine, numbered $32$, that is marked with a red line. Using this identity, we can track the same spine over time and observe whether it persists or disappears. I also drew an outline around the spine, shown in purple, to calculate the sum of fluorescence intensity for the pixels included in this area, which can provide an estimate of the spine’s size. However, this value is not very meaningful by itself as the overall imaging conditions can affect its magnitude and those conditions can change from one imaging session to the next, especially over long periods of time.

I applied two corrections to address this issue. First, I drew a line on the dendrite close to the base of the spine. I assume that the dendrite’s fluorescence intensity remains approximately the same across imaging sessions, so I can normalize spine intensity to the dendrite’s average fluorescence intensity to correct for any longitudinal variations. Additionally, I can refine the measurement by subtracting the background’s average fluorescence intensity, obtained from the pixels included in the olive outline next to the spine. However, for very small spines, the sum of fluorescence intensity of pixels inside the purple outline can be lower than the average background value if the purple outline includes many values close to the background average fluorescence. To correct for this nonlinear artefact that disproportionally affects small spines, I only consider fluorescence values from the purple outline that are above the average background fluorescence. Since I subtract the average background value from pixels in the purple outline I also need to subtract it from the average dendritic fluorescence intensity. The final formula to calculate the corrected, integrated fluorescence intensity of a spine is thus:

$$ i_{spine} = \frac{\sum\limits_{px = 1}^{n} \{ \color{#66c}{i_{px}} | {\color{#66c}{i_{px}} > \color{#660}{\overline{i_{b}}}} \} - \color{#660}{\overline{i_{b}}} } {\color{#0cc}{\overline{i_{d}}} - \color{#660}{\overline{i_{b}}} } $$

where $\color{#66c}{i_{px}}$ is the fluorescence intensity value of each of $n$ pixels inside the purple outline around a spine, $\color{#660}{\overline{i_{b}}}$ is the average pixel value inside the olive outline on the background, and $\color{#0cc}{\overline{i_{d}}}$ is the average pixel value of the teal line along the dendrite.

I used my own customized version of ScanImage, which you can find in a git repository:

In addition to only returning the values for above-background pixels for spines, the most important changes I have implemented are:

  • I fixed a bug where the median of the dendritic intensity was calculated when the option to calculate the mean was chosen, and the reverse was true when the median was chosen.
  • I expanded the allowed range of values for the image lookup tables to include the full range of 16 bit images instead of the hard-coded maximum value of 2000.
  • I remapped the navigation buttons to W, A, S, and D, for up, left, down, and right, respectively. I also set Q and E for navigation along the Z axis of the stack, Space to initiate a line annotation, and R to save annotations to a pre-existing ANN file.

For more details on the changes I introduced, please visit the link above.

Preserving ScanImage headers of an externally processed image stack

It is not unlikely that someone would want to pre-process an image stack before annotating it with ScanImage. For example, filtering or deconvolution can aid the task of visually identifying microscopic structures such as dendritic spines in noisy images. However, ScanImage creates a header when it acquires an image, that consists of a MATLAB structure object which contains information about the acquisition parameters. External programs, such as Fiji, overwrite the header of the file with their own when saving it. If the annotation module of ScanImage fails to find the expected MATLAB structure in the header, it is unable to load the image. When the image file is then re-opened with the annotation module of ScanImage you get an error:

Warning: Failed to open image file: /Path/to/externally_edited_file.tif - Cell contents indices must be greater than 0
> In stackBrowser>loadImage (line 824)
  In stackBrowser>fileBrowseButton_Callback (line 786)
  In gui_mainfcn (line 95)
  In stackBrowser (line 45)

Probably my most popular piece of code, but certainly not the most sophisticated, is a set of two functions that solve this problem. Provided that we still have access to the original TIFF file created by ScanImage during acquisition that’s an easy problem to fix. We just need to extract the original header and append it to the externally processed file.

First, I define a MATLAB function that exports the headers from the original image stacks acquired with ScanImage:

function [] = export_headers(varargin)
%% EXPORT_HEADERS Export headers from TIFF images acquired with ScanImage
%   export_headers() will ask you to select a directory that
%   contains a number of TIFF images acquired with ScanImage, and an
%   output directory in which to save the headers as separate MAT files for
%   each TIFF image stack. Max projection files are ignored.
%   export_headers(directoryIN, directoryOUT) will use
%   directoryIN as the input directory and directoryOUT as the output
%   directory
%   Vassilis Kehayas, November 2016

if isempty(varargin)
    dirIN = uigetdir('', ...
                     'SELECT INPUT DIRECTORY');
    dirOUT = uigetdir(dirIN, ...
                      'SELECT OUTPUT DIRECTORY');
    dirIN = varargin{1};
    dirOUT = varargin{2};

files = listdir(dirIN,1);

for ii = 1:length(files)

    [~, fileName, fileExt] = fileparts(files{ii});

    if ((exist(files{ii}, 'file') ~= 7) ...
        && ~isempty(strfind(fileExt,'tif')) ...
        && isempty((strfind(fileName,'max'))))
        info = imfinfo(files{ii});
        headerInfo = info(1).ImageDescription; %#ok<NASGU>
        save(fullfile(dirOUT, ['h_' fileName]), 'headerInfo')



To insert the original headers back into the edited files, I use another MATLAB function:

function [] = insert_headers(varargin)
%% INSERT_HEADERS Insert headers to TIFF images acquired with ScanImage
%   insert_headers() will ask you to select a directory that contains image
%   headers as MAT files and a directory within which to create a directory
%   called 'with_headers' to store the TIFF files with the original headers
%   inserted. Max projection files are ignored. Any existing contents in
%   the directory 'with_headers' will be deleted before storing the output.
%   insert_headers(headerDir, directoryOUT) will use 'headerDir' as the
%   directory containing the headers and 'directoryOUT' as the output
%   directory.
%   Vassilis Kehayas, November 2016

%% Choose directories
if isempty(varargin)
    headerDir = uigetdir('', ...
                         'SELECT DIRECTORY CONTAINING HEADERS');
    dirOUT = uigetdir(headerDir, ...
    headerDir = varargin{1};
    dirOUT = varargin{2};

display(['Input Directory is ' headerDir])
display(['Output Directory is ' dirOUT])

writeDirOUT = fullfile(dirOUT, 'with_headers');
if exist(writeDirOUT,'dir') ~= 7
    delete([writeDirOUT filesep '*'])

%% Get headers
filesIN = listdir(headerDir, 1);
headerFiles = filesIN(~cellfun(@isempty, ...
                               strfind(filesIN, ...

headers = cell(length(headerFiles), 1);
headerFileName = cell(length(headerFiles), 1);
for ii = 1:length(headerFiles)

    [~, headerFileName{ii}] = fileparts(headerFiles{ii});

    if ((exist(headerFiles{ii}, 'file') ~= 7) ...
        && isempty((strfind(headerFileName{ii}, 'max'))))
        headers{ii} = headerInfo;


%% Get TIFF files and match them to headers
tif2edit = listdir(dirOUT, 1);
counter = 0;
for jj = 1:length(tif2edit)

    [~, tifFileName, tifFileExt] = fileparts(tif2edit{jj});

    if ((exist(tif2edit{jj}, 'file') ~= 7) ...
        && ~isempty(strfind(tifFileExt,'.tif')) ...
        && isempty((strfind(tifFileName,'max'))))
        counter = counter + 1;

        if ~isempty(strfind(headerFileName{counter}, ...
            info = imfinfo(tif2edit{jj});

            % Loop through slices
            for kk = 1 :length(info)

                temp_image = imread(tif2edit{jj}, kk, 'Info', info);
                IMAGE = uint16(temp_image);

                imwrite(IMAGE, ...
                        [writeDirOUT filesep tifFileName '.tif'], ...
                        'Description', headers{counter}, ...
                        'WriteMode', 'append', ...
                        'Compression', 'none');

            error('Name mismatch between header files and images')



The source files for header manipulation are included in a separate git repository:




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