segmentCells3D
Description
specifies options using one or more name-value arguments. For example, specify
labels
= segmentCells3D(cp
,im
,Name=Value
)ImageCellDiameter=50
if the cells in im
are
approximately 50 voxels in diameter.
Note
This functionality requires Deep Learning Toolbox™, Computer Vision Toolbox™, and the Medical Imaging Toolbox™ Interface for Cellpose Library. You can install the Medical Imaging Toolbox Interface for Cellpose Library from Add-On Explorer. For more information about installing add-ons, see Get and Manage Add-Ons.
Examples
Segment 3-D Image Using Cellpose
Create a simulated 3-D microscopy image volume of size 40-by-40-by-40 voxels that contains one cell. The diameter of the simulated cell is approximately 30 voxels.
vol = zeros(40,40,40,"uint8"); vol(15,23,21) = 255; disk = strel("sphere",12); vol = imfilter(vol,double(disk.Neighborhood));
Specify the approximate diameter of the simulated cell, in voxels.
averageCellDiameter = 30;
Create a cellpose
object that configures the pretrained cyto2
model from the Cellpose Library.
cp = cellpose(Model="cyto2");
Segment the image, specifying the average cell diameter and the cell threshold.
labels = segmentCells3D(cp,vol, ... ImageCellDiameter=averageCellDiameter, ... CellThreshold=-6);
Display the simulated image volume with the predicted labels as an overlay. The label mask accurately covers the simulated cell, indicating the model was successful.
volshow(vol,RenderingStyle="SlicePlanes", ... OverlayData=labels, ... OverlayAlpha=0.5);
Input Arguments
cp
— Cellpose model configuration
cellpose
object
Cellpose model configuration, specified as a cellpose
object. The cellpose
object properties specify which model to use, and
define options such as model ensembling and whether to use a GPU.
im
— Intensity image volume
3-D numeric array
Intensity image volume to segment, specified as a 3-D numeric array. The Cellpose
Library converts the values in im
to data type
single
before segmenting the image volume.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| logical
Name-Value Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Example: segmentCells3D(cp,im,ImageCellDiameter=50)
specifies that the
cells in im
are approximately 50 voxels in diameter.
AuxiliaryChannelImage
— Auxiliary channel image volume
zeros(size(im))
(default) | 3-D numeric array
Auxiliary channel image volume, specified as a 3-D numeric array the same size and
data type as im
. You can optionally specify this argument if the
model accepts a nuclear channel. If you do not specify this value, the function passes
an array of zeros the same size as im
to the model. The Cellpose
Library converts the auxiliary channel image volume to data type
single
. This argument corresponds to the chan2
parameter in the Cellpose Library.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| logical
ImageCellDiameter
— Cell diameter in input image volume
positive numeric scalar
Cell diameter in the input image volume im
, specified as a
positive numeric scalar, in voxels. By default, the function assumes that
im
contains cells the same size as in the training data set,
and uses the diameter specified by the TrainingCellDiameter
property of cp
. This argument corresponds to the
diameter
parameter in the Cellpose Library.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| logical
CellThreshold
— Cell probability threshold
0
(default) | numeric scalar
Cell probability threshold, specified as a numeric scalar. The model applies this
threshold to the network output, and includes voxels above the threshold in the label
predictions. A value between -6
and 6
is
suitable for most images. This argument corresponds to the
cellprob_threshold
parameter in the Cellpose Library.
Increasing this value can cause the model to generate cleaner boundaries, but miss some cells.
Decreasing this value can cause the model to detect more cells, but can generate less accurate boundaries between cells.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| logical
NormalizeInput
— Normalize input image volume
true
or 1
(default) | false
or 0
Normalize input image volume, specified as a numeric or
logical 1
(true
) or 0
(false
). When set to true
, the function rescales the input
image volume and the auxiliary channel image volume, if specified, to the range [0,
1]. The function maps the bottom 1% of intensity values of each image volume to
0
and the top 1% of intensity values to 1
.
This argument corresponds to the normalize
parameter in the
Cellpose Library.
Data Types: logical
VoxelSpacing
— Spacing between adjacent image voxels
[1 1 1]
(default) | 3-element numeric row vector
Spacing between adjacent voxels, specified as a 3-element numeric row vector. The
first two elements must be the same, corresponding to equal spacing in the first two
dimensions. Spacing values can correspond to any units, such as millimeters or meters,
but units must be the same for all three dimensions. This argument is used to compute
the anisotropy
parameter in the Cellpose Library.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| logical
TileOverlap
— Overlap between tiles
0.1
(default) | positive numeric scalar
Overlap between tiles, specified as a positive numeric scalar. This argument
specifies the fractional overlap between adjacent tiles. For each voxel in overlapping
regions, the function averages the results across tiles to obtain the final value for
the voxel. A value between 0.05
and 0.5
is
suitable for most images. This argument corresponds to the
tile_overlap
parameter in the Cellpose Library.
Note
The segmentCells3D
function always uses tiling because
the Cellpose Library automatically sets its tile
parameter to
true
for 3-D image volumes.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| logical
TileAndAugment
— Augment image
false
or 0
(default) | true
or 1
Augment images, specified as a numeric or
logical 0
(false
) or 1
(true
). When set to true
, the function sets
TileOverlap
to 0.5
, generating tiles with
50% overlap. The function flips multiple copies of each tile vertically and
horizontally, and then averages results across orientations. Augmentation generally
increases accuracy, but decreases speed. This argument corresponds to the
augment
parameter in the Cellpose Library.
Data Types: logical
GPUBatchSize
— GPU batch size
8
(default) | positive integer
GPU batch size, specified as a positive integer. This argument has an effect only
when processing images on a GPU. The batch size specifies the number of tiles per
batch. Using a larger batch size increases speed, but requires more memory. This
argument corresponds to the batchsize
parameter in the Cellpose
Library.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| logical
PostprocessAtOriginalSize
— Postprocess image at original size
true
or 1
(default) | false
or 0
Postprocess image at its original size, specified as a numeric or
logical 1
(true
) or 0
(false
). When set to true
, the function uses bilinear
interpolation to resize the network outputs to the size of the input image before
postprocessing them. Resizing before postprocessing can generate smoother boundaries
when ImageCellDiameter
is larger than the detectable cell
diameter of the model. When set to false
, the function uses nearest
neighbor interpolation to resize label images to the input size after postprocessing.
This argument is inversely related to the no_resample
parameter in
the Cellpose Library.
Data Types: logical
MinVolume
— Minimum size of each cell
15
(default) | positive numeric scalar
Minimum size of each cell, specified as a positive numeric scalar, in voxels. The
model excludes regions smaller than this value from the final labels, which can reduce
noise. This argument corresponds to the min_size
parameter in the
Cellpose Library.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| logical
Output Arguments
labels
— Segmentation label image
3-D array
Segmentation label image, returned as a 3-D array of data type
double
. Voxel values map to the labels returned by the model. For
example, if the model detects two cells, labels
has values in the
range [0, 2], where background voxels have a value of 0
, the first
cell has voxel values of 1
, and the second cell has voxel values of
2
.
References
[1] Stringer, Carsen, Tim Wang, Michalis Michaelos, and Marius Pachitariu. “Cellpose: A Generalist Algorithm for Cellular Segmentation.” Nature Methods 18, no. 1 (January 2021): 100–106. https://doi.org/10.1038/s41592-020-01018-x.
[2] Pachitariu, Marius, and Carsen Stringer. “Cellpose 2.0: How to Train Your Own Model.” Nature Methods 19, no. 12 (December 2022): 1634–41. https://doi.org/10.1038/s41592-022-01663-4.
Version History
Introduced in R2023b
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