A scalable library for calculating features from intensity-label image data
Nyxus is a feature-rich, highly optimized, Python/C++ application capable of analyzing images of arbitrary size and assembling complex regions of interest (ROIs) split across multiple image tiles and files. This accomplished through multi-threaded tile prefetching and a three phase analysis pipeline shown below.
Nyxus can be used via Python or command line and is available in containerized form for reproducible execution. Nyxus computes over 450 combined intensity, texture, and morphological features at the ROI or whole image level with more in development. Key features that make Nyxus unique among other image feature extraction applications is its ability to operate at any scale, its highly validated algorithms, and its modular nature that makes the addition of new features straightforward.
Currently, Nyxus can read image data from OME-TIFF, OME-Zarr and DICOM 2D Grayscale images. It also has a Python API to support in-memory image data via Numpy array.
The docs can be found at Read the Docs.
For use in python, the latest version of Nyxus can be installed via the Pip package manager or Conda package manager:
pip install nyxus
or
conda install nyxus -c conda-forge
Usage is very straightforward. Given intensities and labels folders, Nyxus pairs up intensity-label images and extracts features from all of them. A summary of the available feature are listed below.
from nyxus import Nyxus
nyx = Nyxus(["*ALL*"])
intensityDir = "/path/to/images/intensities/"
maskDir = "/path/to/images/labels/"
features = nyx.featurize_directory (intensityDir, maskDir)
Alternatively, Nyxus can process explicitly defined pairs of intensity-mask images thus specifying custom 1:N and M:N mapping between label and intensity image files. The following example extracts features from intensity images 'i1', 'i2', and 'i3' related with mask images 'm1' and 'm2' via a custom mapping:
from nyxus import Nyxus
nyx = Nyxus(["*ALL*"])
features = nyx.featurize_files(
[
"/path/to/images/intensities/i1.ome.tif",
"/path/to/images/intensities/i2.ome.tif",
"/path/to/images/intensities/i3.ome.tif"
],
[
"/path/to/images/labels/m1.ome.tif",
"/path/to/images/labels/m2.ome.tif",
"/path/to/images/labels/m2.ome.tif"
],
False)
The features variable is a Pandas dataframe similar to what is shown below.
| mask_image | intensity_image | label | MEAN | MEDIAN | ... | GABOR_6 | |
|---|---|---|---|---|---|---|---|
| 0 | p1_y2_r51_c0.ome.tif | p1_y2_r51_c0.ome.tif | 1 | 45366.9 | 46887 | ... | 0.873016 |
| 1 | p1_y2_r51_c0.ome.tif | p1_y2_r51_c0.ome.tif | 2 | 27122.8 | 27124.5 | ... | 1.000000 |
| 2 | p1_y2_r51_c0.ome.tif | p1_y2_r51_c0.ome.tif | 3 | 34777.4 | 33659 | ... | 0.942857 |
| 3 | p1_y2_r51_c0.ome.tif | p1_y2_r51_c0.ome.tif | 4 | 35808.2 | 36924 | ... | 0.824074 |
| 4 | p1_y2_r51_c0.ome.tif | p1_y2_r51_c0.ome.tif | 5 | 36739.7 | 37798 | ... | 0.854067 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 734 | p5_y0_r51_c0.ome.tif | p5_y0_r51_c0.ome.tif | 223 | 54573.3 | 54573.3 | ... | 0.980769 |
Nyxus can also process intensity-mask pairs that are loaded as Numpy arrays using the featurize method. This method takes in either a single pair of 2D intensity-mask pairs
or a pair of 3D arrays containing 2D intensity and mask images. There is also two optional parameters to supply names to the resulting dataframe, .
from nyxus import Nyxus
import numpy as np
nyx = Nyxus(["*ALL*"])
intens = np.array([
[[1, 4, 4, 1, 1],
[1, 4, 6, 1, 1],
[4, 1, 6, 4, 1],
[4, 4, 6, 4, 1]],
])
seg = np.array([
[[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[0, 1, 1, 1, 1],
[1, 1, 1, 1, 1]]
])
features = nyx.featurize(intens, seg)
The features variable is a Pandas dataframe similar to what is shown below.
| mask_image | intensity_image | label | MEAN | MEDIAN | ... | GABOR_6 | |
|---|---|---|---|---|---|---|---|
| 0 | Segmentation1 | Intensity1 | 1 | 45366.9 | 46887 | ... | 0.873016 |
| 1 | Segmentation1 | Intensity1 | 2 | 27122.8 | 27124.5 | ... | 1.000000 |
| 2 | Segmentation1 | Intensity1 | 3 | 34777.4 | 33659 | ... | 0.942857 |
| 3 | Segmentation1 | Intensity1 | 4 | 35808.2 | 36924 | ... | 0.824074 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 14 | Segmentation2 | Intensity2 | 6 | 54573.3 | 54573.3 | ... | 0.980769 |
Note that in this case, default names of virtual image files were provided for the mask_image and intensity_image columns. To override default names 'Intensity' and 'Segmentation' appearing in these columns, the optional arguments intensity_names and label_names are used by passing lists of names in.
The length of the lists must be the same as the length of the mask and intensity arrays. The following example sets mask and intensity images in the output to desired values:
intens_names = ['int1', 'int2']
seg_names = ['seg1', 'seg2']
features = nyx.featurize(intens, seg, intens_name, seg_name)
The features variable will now use the custom names, as shown below
| mask_image | intensity_image | label | MEAN | MEDIAN | ... | GABOR_6 | |
|---|---|---|---|---|---|---|---|
| 0 | seg1 | int1 | 1 | 45366.9 | 46887 | ... | 0.873016 |
| 1 | seg1 | int1 | 2 | 27122.8 | 27124.5 | ... | 1.000000 |
| 2 | seg1 | int1 | 3 | 34777.4 | 33659 | ... | 0.942857 |
| 3 | seg1 | int1 | 4 | 35808.2 | 36924 | ... | 0.824074 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 14 | seg2 | int2 | 6 | 54573.3 | 54573.3 | ... | 0.980769 |
For more information on all of the available options and features, check out the documentation.
Nyxus can also be built from source and used from the command line, or via a pre-built Docker container.
All parameters to configure Nyxus are available to set within the constructor. These parameters can also be updated after the object is created using the set_params
method. This method takes in keyword arguments where the key is a valid parameter in Nyxus and the value is the updated value for the parameter. For example,
to update the coarse_gray_depth to 256 and the gabor_f0 parameter to 0.1, the following can be done:
from nyxus import Nyxus
nyx = Nyxus(["*ALL*"])
intensityDir = "/path/to/images/intensities/"
maskDir = "/path/to/images/labels/"
features = nyx.featurize_directory (intensityDir, maskDir)
nyx.set_params(coarse_gray_depth=256, gabor_f0=0.1)
A list of valid parameters is included in the documentation for this method.
To get the values of the parameters in Nyxus, the get_params method is used. If no arguments are passed to this function, then a dictionary mapping all of the variable names to the respective value is returned. For example,
from nyxus import Nyxus
nyx = Nyxus(["*ALL*"])
intensityDir = "/path/to/images/intensities/"
maskDir = "/path/to/images/labels/"
features = nyx.featurize_directory (intensityDir, maskDir)
print(nyx.get_params())
will print the dictionary
{'coarse_gray_depth': 256,
'features': ['*ALL*'],
'gabor_f0': 0.1,
'gabor_freqs': [1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0],
'gabor_gamma': 0.1,
'gabor_kersize': 16,
'gabor_sig2lam': 0.8,
'gabor_theta': 45.0,
'gabor_thold': 0.025,
'ibsi': 0,
'n_loader_threads': 1,
'n_feature_calc_threads': 4,
'neighbor_distance': 5,
'pixels_per_micron': 1.0}
There is also the option to pass arguments to this function to only receive a subset of parameter values. The arguments should be valid parameter names as string, separated by commas. For example,
from nyxus import Nyxus
nyx = Nyxus(["*ALL*"])
intensityDir = "/path/to/images/intensities/"
maskDir = "/path/to/images/labels/"
features = nyx.featurize_directory (intensityDir, maskDir)
print(nyx.get_params('coarse_gray_depth', 'features', 'gabor_f0'))
will print the dictionary
{
'coarse_gray_depth': 256,
'features': ['*ALL*'],
'gabor_f0': 0.1
}
Nyxus provides the ability to get the results of the feature calculations in Arrow IPC and Parquet formats.
To create an Arrow IPC or Parquet file, use output_type="arrowipc" or output_type="parquet" in Nyxus.featurize* calls.
Optionally, an output_path argument can be passed to specify the location of the output file. For example,
from nyxus import Nyxus
import numpy as np
intens = np.array([
[[1, 4, 4, 1, 1],
[1, 4, 6, 1, 1],
[4, 1, 6, 4, 1],
[4, 4, 6, 4, 1]],
[[1, 4, 4, 1, 1],
[1, 1, 6, 1, 1],
[1, 1, 3, 1, 1],
[4, 4, 6, 1, 1]],
[[1, 4, 4, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 6, 1, 1],
[1, 1, 6, 1, 1]],
[[1, 4, 4, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 6, 1, 1]],
])
seg = np.array([
[[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1]],
[[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[0, 1, 1, 1, 1],
[1, 1, 1, 1, 1]],
[[1, 1, 1, 0, 0],
[1, 1, 1, 1, 1],
[1, 1, 0, 1, 1],
[1, 1, 1, 1, 1]],
[[1, 1, 1, 0, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1]]
])
nyx = Nyxus(["*ALL_INTENSITY*"])
arrow_file = nyx.featurize(intens, seg, output_type="arrowipc", output_path="some_path")
print(arrow_file)
The output is:
NyxusFeatures.arrow
This functionality is also available in the through the command line using the flag --outputType. If this flag is set to --outputType=arrowipc then the results will be written to an Arrow IPC file in the output directory and --outputType=parquet will write to a Parquet file.
The feature extraction plugin extracts morphology and intensity based features from pairs of intensity/binary mask images and produces a csv file output. The input image should be in tiled OME TIFF format. The plugin extracts the following features:
Nyxus provides a set of pixel intensity, morphology, texture, intensity distribution features, digital filter based features and image moments
| Nyxus feature code | Description |
|---|---|
| INTEGRATED_INTENSITY | Integrated intensity of the region of interest (ROI) |
| MEAN, MAX, MEDIAN, STANDARD_DEVIATION, MODE | Mean/max/median/stddev/mode intensity value of the ROI |
| SKEWNESS, KURTOSIS, HYPERSKEWNESS, HYPERFLATNESS | higher standardized moments |
| MEAN_ABSOLUTE_DEVIATION | Mean absolute deviation |
| ENERGY | ROI energy |
| ROOT_MEAN_SQUARED | Root of mean squared deviation |
| ENTROPY | ROI entropy - a measure of the amount of information in the ROI |
| UNIFORMITY | Uniformity - measures how uniform the distribution of ROI intensities is |
| UNIFORMITY_PIU | Percent image uniformity, another measure of intensity distribution uniformity |
| P01, P10, P25, P75, P90, P99 | 1%, 10%, 25%, 75%, 90%, and 99% percentiles of intensity distribution |
| INTERQUARTILE_RANGE | Distribution's interquartile range |
| ROBUST_MEAN_ABSOLUTE_DEVIATION | Robust mean absolute deviation |
| MASS_DISPLACEMENT | ROI mass displacement |
| AREA_PIXELS_COUNT | ROI area in the number of pixels |
| COMPACTNESS | Mean squared distance of the object’s pixels from the centroid divided by the area |
| BBOX_YMIN | Y-position and size of the smallest axis-aligned box containing the ROI |
| BBOX_XMIN | X-position and size of the smallest axis-aligned box containing the ROI |
| BBOX_HEIGHT | Height of the smallest axis-aligned box containing the ROI |
| BBOX_WIDTH | Width of the smallest axis-aligned box containing the ROI |
| MAJOR/MINOR_AXIS_LENGTH, ECCENTRICITY, ORIENTATION, ROUNDNESS | Inertia ellipse features |
| NUM_NEIGHBORS, PERCENT_TOUCHING | The number of neighbors bordering the ROI's perimeter and related neighbor methods |
| EXTENT | Proportion of the pixels in the bounding box that are also in the region |
| CONVEX_HULL_AREA | Area of ROI's convex hull |
| SOLIDITY | Ratio of pixels in the ROI common with its convex hull image |
| PERIMETER | Number of pixels in ROI's contour |
| EQUIVALENT_DIAMETER | Diameter of the circle having circumference equal to the ROI's perimeter |
| EDGE_MEAN/MAX/MIN/STDDEV_INTENSITY | Intensity statistics of ROI's contour pixels |
| CIRCULARITY | Represents how similar a shape is to circle. Clculated based on ROI's area and its convex perimeter |
| EROSIONS_2_VANISH | Number of erosion operations for a ROI to vanish in its axis aligned bounding box |
| EROSIONS_2_VANISH_COMPLEMENT | Number of erosion operations for a ROI to vanish in its convex hull |
| FRACT_DIM_BOXCOUNT, FRACT_DIM_PERIMETER | Fractal dimension features |
| GLCM | Grey level co-occurrence Matrix features |
| GLRLM | Grey level run-length matrix based features |
| GLDZM | Grey level distance zone matrix based features |
| GLSZM | Grey level size zone matrix based features |
| GLDM | Grey level dependency matrix based features |
| NGTDM | Neighbouring grey tone difference matrix features |
| ZERNIKE2D, FRAC_AT_D, RADIAL_CV, MEAN_FRAC | Radial distribution features |
| GABOR | A set of Gabor filters of varying frequencies and orientations |
For the complete list of features see Nyxus provided features
Apart from defining your feature set by explicitly specifying comma-separated feature code, Nyxus lets a user specify popular feature groups. Supported feature groups are:
| Group code | Belonging features |
|---|---|
| *all_intensity* | integrated_intensity, mean, median, min, max, range, standard_deviation, standard_error, uniformity, skewness, kurtosis, hyperskewness, hyperflatness, mean_absolute_deviation, energy, root_mean_squared, entropy, mode, uniformity, p01, p10, p25, p75, p90, p99, interquartile_range, robust_mean_absolute_deviation, mass_displacement |
| *all_morphology* | area_pixels_count, area_um2, centroid_x, centroid_y, weighted_centroid_y, weighted_centroid_x, compactness, bbox_ymin, bbox_xmin, bbox_height, bbox_width, major_axis_length, minor_axis_length, eccentricity, orientation, num_neighbors, extent, aspect_ratio, equivalent_diameter, convex_hull_area, solidity, perimeter, edge_mean_intensity, edge_stddev_intensity, edge_max_intensity, edge_min_intensity, circularity |
| *basic_morphology* | area_pixels_count, area_um2, centroid_x, centroid_y, bbox_ymin, bbox_xmin, bbox_height, bbox_width |
| *geomoms* | shape and intensity geometric moments, equivalent to *igeomoms* and *sgeomoms* combined |
| *igeomoms* | intensity raw moments IMOM_RM_pq, central moments IMOM_CM_pq, normalized raw moments IMOM_NRM_pq, normalized central moments IMOM_NCM_pq, Hu invariants IMOM_HUk, weighted raw moments IMOM_WRM_pq, weighted central moments IMOM_WCM_pq, weighted normalized central moments IMOM_WNCM_pq, weighted Hu invariants IMOM_WHUk |
| *sgeomoms* | shape raw moments SPAT_MOMENT_pq, central moments CENTRAL_MOMENT_pq, normalized raw moments NORM_SPAT_MOMENT_pq, normalized central moments NORM_CENTRAL_MOMENT_pq, Hu invariants HU_Mk, weighted raw moments WEIGHTED_SPAT_MOMENT_pq, weighted central moments WEIGHTED_CENTRAL_MOMENT_pq, weighted normalized central moments WT_NORM_CTR_MOM_pq, weighted Hu invariants WEIGHTED_HU_Mk |
| *all_glcm* | glcm_asm, glcm_acor, glcm_cluprom, glcm_clushade, glcm_clutend, glcm_contrast, glcm_correlation, glcm_difave, glcm_difentro, glcm_difvar, glcm_dis, glcm_energy, glcm_entropy, glcm_hom1, glcm_hom2, glcm_id, glcm_idn, glcm_idm, glcm_idmn, glcm_infomeas1, glcm_infomeas2, glcm_iv, glcm_jave, glcm_je, glcm_jmax, glcm_jvar, glcm_sumaverage, glcm_sumentropy, glcm_sumvariance, glcm_variance |
| *all_glrlm* | glrlm_sre, glrlm_lre, glrlm_gln, glrlm_glnn, glrlm_rln, glrlm_rlnn, glrlm_rp, glrlm_glv, glrlm_rv, glrlm_re, glrlm_lglre, glrlm_hglre, glrlm_srlgle, glrlm_srhgle, glrlm_lrlgle, glrlm_lrhgle |
| *all_glszm* | glszm_sae, glszm_lae, glszm_gln, glszm_glnn, glszm_szn, glszm_sznn, glszm_zp, glszm_glv, glszm_zv, glszm_ze, glszm_lglze, glszm_hglze, glszm_salgle, glszm_sahgle, glszm_lalgle, glszm_lahgle |
| *all_gldm* | gldm_sde, gldm_lde, gldm_gln, gldm_dn, gldm_dnn, gldm_glv, gldm_dv, gldm_de, gldm_lgle, gldm_hgle, gldm_sdlgle, gldm_sdhgle, gldm_ldlgle, gldm_ldhgle |
| *all_ngtdm* | ngtdm_coarseness, ngtdm_contrast, ngtdm_busyness, ngtdm_complexity, ngtdm_strength |
| *wholeslide* | All the features except those irrelevant for the whole-slide use case (BasicMorphology, EnclosingInscribingCircumscribingCircle, ConvexHull, FractalDimension, GeodeticLengthThickness, Neighbor, RoiRadius, EllipseFitting, EulerNumber, Extrema, ErosionPixel, CaliperFeret, CaliperMartin, CaliperNassenstein, and Chords) |
| *all* | All the features |
Assuming you built the Nyxus binary as outlined below, the following parameters are available for the command line interface:
Parameter | Description | Type |
|---|---|---|
| --outputType | Output type for feature values (speratecsv, singlecsv, arrow, parquet). Default value: '--outputType=separatecsv' | string constant |
| --features | String constant or comma-seperated list of constants requesting a group of features or particular feature. Default value: '--features=*ALL*' | string |
| --filePattern | Regular expression to match image files in directories specified by parameters '--intDir' and '--segDir'. To match all the files, use '--filePattern=.*' | string |
| --intDir | Directory of intensity image collection | path |
| --outDir | Output directory | path |
| --segDir | Directory of labeled image collection | path |
| --coarseGrayDepth | (optional) Custom number of greyscale level bins used in texture features. Default: '--coarseGrayDepth=256' | integer |
| --glcmAngles | (optional) Enabled direction angles of the GLCM feature. Superset of values: 0, 45, 90, and 135. Default: '--glcmAngles=0,45,90,135' | list of integer constants |
| --intSegMapDir | (optional) Data collection of the ad-hoc intensity-to-mask file mapping. Must be used in combination with parameter '--intSegMapFile' | path |
| --intSegMapFile | (optional) Name of the text file containing an ad-hoc intensity-to-mask file mapping. The files are assumed to reside in corresponding intensity and label collections. Must be used in combination with parameter '--intSegMapDir' | string |
| --pixelDistance | (optional) Number of pixels to treat ROIs within specified distance as neighbors. Default value: '--pixelDistance=5' | integer |
| --pixelsPerCentimeter | (optional) Number of pixels in centimeter used by unit length-related features. Default value: 0 | real |
| --ramLimit | (optional) Amount of memory not to exceed by Nyxus, in megabytes. Default value: 50% of available memory. Example: '--ramLimit=2000' to use 2,000 megabytes | integer |
| --reduceThreads | (optional) Number of CPU threads used on the feature calculation step. Default: '--reduceThreads=1' | integer |
| --skiproi | (optional) Skip ROIs having specified labels. Example: '--skiproi=image1.tif:2,3,4;image2.tif:45,56' | string |
| --tempDir | (optional) Directory used by temporary out-of-RAM objects. Default value: system temporary directory | path |
| --hsig | (optional) Channel signature Example: "--hsig=_c" to match images whose file names have channel info starting substring '_c' like in 'p0_y1_r1_c1.ome.tiff' | string |
| --hpar | (optional) Channel number that should be used as a provider of parent segments. Example: '--hpar=1' | integer |
| --hchi | (optional) Channel number that should be used as a provider of child segments. Example: '--hchi=0' | integer |
| --hag | (optional) Name of a method how to aggregate features of segments recognized as children of same parent segment. Valid options are 'SUM', 'MEAN', 'MIN', 'MAX', 'WMA' (weighted mean average), and 'NONE' (no aggregation, instead, same parent child segments will be laid out horizontally) | string |
| --fpimgdr | (optional) Desired dynamic range of voxels of a floating point TIFF image. Example: --fpimgdr=240 makes intensities be read in range 0-240. Default value: 10e4 | unsigned integer |
| --fpimgmin | (optional) Minimum intensity of voxels of a floating point TIFF image. Default value: 0.0 | real |
| --fpimgdr | (optional) Maximum intensity of voxels of a floating point TIFF image. Default value: 1.0 | real |
Example 1: Running Nyxus to process images of specific image channel
Suppose we need to process intensity/mask images of channel 1 :
./nyxus --features=*all_intensity*,*basic_morphology* --intDir=/path/to/intensity/images --segDir=/path/to/mask/images --outDir=/path/to/output --filePattern=.*_c1\.ome\.tif --outputType=singlecsv
Example 2: Running Nyxus to process specific image
Suppose we need to process intensity/mask file p1_y2_r68_c1.ome.tif :
./nyxus --features=*all_intensity*,*basic_morphology* --intDir=/path/to/intensity/images --segDir=/path/to/mask/images --outDir=/path/to/output --filePattern=p1_y2_r68_c1\.ome\.tif --outputType=singlecsv
Example 3: Running Nyxus to extract only intensity and basic morphology features
./nyxus --features=*all_intensity*,*basic_morphology* --intDir=/path/to/intensity/images --segDir=/path/to/mask/images --outDir=/path/to/output --filePattern=.* --outputType=singlecsv
Example 4: Skipping specified ROIs while extracting features
Suppose we need to blacklist ROI labels 2 and 3 from the kurtosis feature extraction globally, in each image. The command line way to do that is using option --skiproi :
./nyxus --skiproi=2,3 --features=KURTOSIS --intDir=/path/to/intensity/images --segDir=/path/to/mask/images --outDir=/path/to/output --filePattern=.* --outputType=singlecsv
As a result, the default feature extraction result produced without option --skiproi looking like
mask_image intensity_image label KURTOSIS 0 p0_y1_r1_c0.tif p0_y1_r1_c0.tif 1 -0.134216 1 p0_y1_r1_c0.tif p0_y1_r1_c0.tif 2 -0.130024 2 p0_y1_r1_c0.tif p0_y1_r1_c0.tif 3 -1.259801 3 p0_y1_r1_c0.tif p0_y1_r1_c0.tif 4 -0.934786 4 p0_y1_r1_c0.tif p0_y1_r1_c0.tif 5 -1.072111 .. ... ... ... ...
will start looking like
mask_image intensity_image label KURTOSIS 0 p0_y1_r1_c0.tif p0_y1_r1_c0.tif 1 -0.134216 1 p0_y1_r1_c0.tif p0_y1_r1_c0.tif 4 -0.934786 2 p0_y1_r1_c0.tif p0_y1_r1_c0.tif 5 -1.072111 3 p0_y1_r1_c0.tif p0_y1_r1_c0.tif 6 -0.347741 4 p0_y1_r1_c0.tif p0_y1_r1_c0.tif 7 -1.283468 .. ... ... ... ...
Note the comma character separator , in the blacklisted ROI label list.
If we need to blacklist ROI labels 15 and 16 only in image image421.tif ROI label 17 in image image422.tif, we can do it via a per-file blacklist :
./nyxus --skiproi=image421.tif:15,16;image421.tif:17 --features=KURTOSIS --intDir=/path/to/intensity/images --segDir=/path/to/mask/images --outDir=/path/to/output --filePattern=.* --outputType=singlecsv
Note the colon character : between the file name and backlisted labels within this file and semicolon character separator ; of file blacklists.
Example 5: Skipping specified ROIs while extracting features (via Python API)
The Nyxus Python API equivalent of global ROI blacklisting is implemented by method blacklist_roi(string) called before a call of method featurize...(), for example, labels 15, 16, and 17 can be globally blacklisted as follows:
from nyxus import Nyxus
nyx = Nyxus(features=["KURTOSIS"])
nyx.blacklist_roi('15,16,17')
features = nyx.featurize_directory (intensity_dir="/path/to/intensity/images", label_dir="/path/to/mask/images", file_pattern=".*")
Similarly, per-file ROI blacklists are defined in a way similar to the command line interface:
from nyxus import Nyxus
nyx = Nyxus(features=["KURTOSIS"])
nyx.blacklist_roi('p0_y1_r1_c0.ome.tif:15,16;p0_y1_r2_c0.ome.tif:17')
features = nyx.featurize_directory (intensity_dir="/path/to/intensity/images", label_dir="/path/to/mask/images", file_pattern=".*")
See also methods clear_roi_blacklist() and roi_blacklist_get_summary() .
Hierarchical ROI analysis in a form of finding ROIs nested geometrically as nested AABBs and aggregating features of child ROIs within corresponding parent is available as an optional extra step after the feature extraction of the whole image set is finished. To enable this step, all the command line options '--hsig', '--hpar', '--hchi', and '--hag' need to have non-blank valid values.
Valid aggregation options are SUM, MEAN, MIN, MAX, WMA (weighted mean average), or NONE (no aggregation).
Example 6: Processing an image set with nested ROI postprocessing
nyxus --features=*ALL_intensity* --intDir=/path/to/intensity/images --segDir=/path/to/mask/images --outDir=/path/to/output/directory --filePattern=.* --outputType=separatecsv --reduceThreads=4 --hsig=_c --hpar=1 --hchi=0 --hag=WMA
As a result, 2 additional CSV files will be produced for each mask image whose channel number matches the value of option '--hpar': file
<imagename>_nested_features.csv
where features of the detected child ROIs are laid next to their parent ROIs on same lines and auxiliary file
<imagename>_nested_relations.csv
serving as a relational table of parent and child ROI labels within parent ROI channel image <imagename>.
The nested features functionality can also be utilized in Python using the Nested class in nyxus. The Nested class
contains two methods, find_relations and featurize.
The find_relations method takes in a path to the label files, along with a child
filepattern to identify the files in the child channel and a parent filepattern to match the files in the parent channel. The find_relation method
returns a Pandas DataFrame containing a mapping between parent ROIs and the respective child ROIs.
The featurize method takes in the parent-child mapping along with the features of the ROIs in the child channel. If a list of aggregate functions
is provided to the constructor, this method will return a pivoted DataFrame where the rows are the ROI labels and the columns are grouped by the features.
Example 7: Using aggregate functions
from nyxus import Nyxus, Nested
import numpy as np
int_path = 'path/to/intensity'
seg_path = 'path/to/segmentation'
nyx = Nyxus(['GABOR'])
child_features = nyx.featurize(int_path, seg_path, file_pattern='p[0-9]_y[0-9]_r[0-9]_c0\.ome\.tif')
nest = Nested(['sum', 'mean', 'min', ('nanmean', lambda x: np.nanmean(x))])
df = nest.find_relations(seg_path, 'p{r}_y{c}_r{z}_c1.ome.tif', 'p{r}_y{c}_r{z}_c0.ome.tif')
df2 = nest.featurize(df, features)
The parent-child map is
Image Parent_Label Child_Label
0 /path/to/image 72 65
1 /path/to/image 71 66
2 /path/to/image 70 64
3 /path/to/image 68 61
4 /path/to/image 67 65
and the aggregated DataFrame is
GABOR_0 GABOR_1 GABOR_2 ...
sum mean min nanmean sum mean min nanmean sum mean ...
label ...
1 24.010227 0.666951 0.000000 0.666951 19.096262 0.530452 0.001645 0.530452 17.037345 0.473260 ...
2 13.374170 0.445806 0.087339 0.445806 7.279187 0.242640 0.075000 0.242640 6.390529 0.213018 ...
3 5.941783 0.198059 0.000000 0.198059 3.364149 0.112138 0.000000 0.112138 2.426409 0.080880 ...
4 13.428773 0.559532 0.000000 0.559532 12.021938 0.500914 0.008772 0.500914 9.938915 0.414121 ...
5 6.535722 0.181548 0.000000 0.181548 1.833463 0.050930 0.000000 0.050930 2.083023 0.057862 ...
Example 8: Without aggregate functions
from nyxus import Nyxus, Nested
import numpy as np
int_path = 'path/to/intensity'
seg_path = 'path/to/segmentation'
nyx = Nyxus(['GABOR'])
child_features = nyx.featurize(int_path, seg_path, file_pattern='p[0-9]_y[0-9]_r[0-9]_c0\.ome\.tif')
nest = Nested()
df = nest.find_relations(seg_path, 'p{r}_y{c}_r{z}_c1.ome.tif', 'p{r}_y{c}_r{z}_c0.ome.tif')
df2 = nest.featurize(df, features)
the parent-child map remains the same but the featurize result becomes
GABOR_0 ...
Child_Label 1 2 3 4 5 6 7 8 9 10 ...
label ...
1 0.666951 NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
2 NaN 0.445806 NaN NaN NaN NaN NaN NaN NaN NaN ...
3 NaN NaN 0.198059 NaN NaN NaN NaN NaN NaN NaN ...
4 NaN NaN NaN 0.559532 NaN NaN NaN NaN NaN NaN ...
5 NaN NaN NaN NaN 0.181548 NaN NaN NaN NaN NaN ...
Nyxus uses CMake as the build system and needs a C++17 supported compiler to build from the source.
To build Nyxus from source, several build dependencies are needed to be satisfied. These dependencies arise from Nyxus's need to read and write various data format. The dependencies are listed below.
These packages also have underlying dependencies and at times, these dependency resolution may appear challenging. We prefer conda to help with resolving these dependencies. However, for users without access to a conda enviornment, we have also provided installation script to build and install all the dependencies except Apache Arrow.
By default, Nyxus can be built with a minimal set of dependecies (Tiff support and Python interface). To build Nyxus with all the supported IO options mentioned above, pass -DALLEXTRAS=ON in the cmake command.
Nyxus also can be build with NVIDIA GPU support. To do so, a CUDA Development toolkit compatible with the host C++ compiler need to be present in the system. For building with GPU support, pass -DUSEGPU=ON flag in the cmake command.
To build the command line interface, pass -DBUILD_CLI=ON in the cmake command.
Below is an example of how to build Nyxus inside a conda environment on Linux.
conda create -n nyxus_build python=3.10
conda activate nyxus_build
git clone https://github.com/PolusAI/nyxus.git
cd nyxus
conda install mamba -c conda-forge
mamba install -y -c conda-forge --file ci-utils/envs/conda_cpp.txt
export NYXUS_DEP_DIR=$CONDA_PREFIX
mkdir build
cd build
cmake -DBUILD_CLI=ON -DALLEXTRAS=ON -DUSEGPU=ON ..
make -j4
To install the python package in the conda environment on Linux, use the following direction.
conda create -n nyxus_build python=3.10
conda activate nyxus_build
git clone https://github.com/PolusAI/nyxus.git
cd nyxus
conda install mamba -c conda-forge
mamba install -y -c conda-forge --file ci-utils/envs/conda_cpp.txt --file ci-utils/envs/conda_py.txt
export NYXUS_DEP_DIR=$CONDA_PREFIX
CMAKE_ARGS="-DUSEGPU=ON -DALLEXTRAS=ON -DPython_ROOT_DIR=$CONDA_PREFIX -DPython_FIND_VIRTUALENV=ONLY" python -m pip install . -vv
To build Nyxus outside of a conda environment, we will first need to build and install all the required and optional dependecies. ci-utils/install_prereq_windwos.bat and ci-utils/install_prereq_linux.sh performs the task for Windows and Linux (and Mac) respectively. These script take a --min_build yes option to only build the minimal dependencies. Below, we provide an example for Windows OS.
git clone https://github.com/PolusAI/nyxus.git
cd nyxus
mkdir build
cd build
..\ci-utils\install_prereq_windows.bat
cmake -DBUILD_CLI=ON -DUSEGPU=ON -DALLEXTRAS=ON -DCMAKE_PREFIX_PATH=.\local_install -DCMAKE_INSTALL_PREFIX=.\local_install ..
cmake --build . --config Release
set PATH=%PATH%;%cd%\local_install\bin
To install the python package in the environment on Linux, use the following direction.
python -m virtualenv venv
venv\Scripts\activate.bat
git clone https://github.com/PolusAI/nyxus.git
cd nyxus
mkdir build_dep
cd build_dep
..\ci-utils\install_prereq_windows.bat
cd ..
set NYXUS_DEP_DIR=%cd%\build_dep\local_install
set CMAKE_ARGS=-DUSEGPU=ON -DALLEXTRAS=ON
python -m pip install . -vv
xcopy /E /I /y %NYXUS_DEP_DIR%\bin\*.dll %VIRTUAL_ENV%\lib\site-packages\nyxus
Note that, in both cases, the dlls of the dependencies need to be in the PATH (for CLI) or in the site-packages location (for Python package).
Running Nyxus from a local directory freshly made Docker container is a good idea. It allows one to test-run conteinerized Nyxus before it reaches Docker cloud deployment.
To search available Nyxus images run command
docker search nyxus
and you'll be shown that it's available at least via organization 'polusai'. To pull it, run
docker pull polusai/nyxus
The following command line is an example of running the dockerized feature extractor (image hash 87f3b560bbf2) with only intensity features selected:
docker run -it [--gpus all] --mount type=bind,source=/images/collections,target=/data 87f3b560bbf2 --intDir=/data/c1/int --segDir=/data/c1/seg --outDir=/data/output --filePattern=.* --outputType=separatecsv --features=entropy,kurtosis,skewness,max_intensity,mean_intensity,min_intensity,median,mode,standard_deviation
Nyxus is available as plugin for WIPP.
Label image collection: The input should be a labeled image in tiled OME TIFF format (.ome.tif). Extracting morphology features, Feret diameter statistics, neighbors, hexagonality and polygonality scores requires the segmentation labels image. If extracting morphological features is not required, the label image collection can be not specified.
Intensity image collection: Extracting intensity-based features requires intensity image in tiled OME TIFF format. This is an optional parameter - the input for this parameter is required only when intensity-based features needs to be extracted.
File pattern: Enter file pattern to match the intensity and labeled/segmented images to extract features (https://pypi.org/project/filepattern/) Filepattern will sort and process files in the labeled and intensity image folders alphabetically if universal selector(.*.ome.tif) is used. If a more specific file pattern is mentioned as input, it will get matches from labeled image folder and intensity image folder based on the pattern implementation.
Pixel distance: Enter value for this parameter if neighbors touching cells needs to be calculated. The default value is 5. This parameter is optional.
Features: Comma separated list of features to be extracted. If all the features are required, then choose option all.
Outputtype: There are 4 options available under this category. Separatecsv - to save all the features extracted for each image in separate csv file. Singlecsv - to save all the features extracted from all the images in the same csv file. Arrow - to save all the features extracted from all the images in Apache Arrow format. Parquet - to save all the features extracted from all the images in Apache Parquet format
Embedded pixel size: This is an optional parameter. Use this parameter only if units are present in the metadata and want to use those embedded units for the features extraction. If this option is selected, value for the length of unit and pixels per unit parameters are not required.
Length of unit: Unit name for conversion. This is also an optional parameter. This parameter will be displayed in plugin's WIPP user interface only when embedded pixel size parameter is not selected (ckrresponding check box checked).
Pixels per unit: If there is a metric mentioned in Length of unit, then Pixels per unit cannot be left blank and hence the scale per unit value must be mentioned in this parameter. This parameter will be displayed in plugin's user interface only when embedded pixel size parameter is not selected.
Note: If Embedded pixel size is not selected and values are entered in Length of unit and Pixels per unit, then the metric unit mentioned in length of unit will be considered. If Embedded pixel size, Length of unit and Pixels per unit is not selected and the unit and pixels per unit fields are left blank, the unit will be assumed to be pixels.
Output: The output is a csv file containing the value of features required.
For more information on WIPP, visit the official WIPP page.
FAQs
Scalable library for calculating features from intensity-label image data
We found that nyxus demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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