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1) Separate instance filtering and statistic measurement ; 2) Add sph…
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…ericity (3D), perimeter/surface area (2D/3D) and elongation (2D) calculations using the same formulas as described in MorphoLibJ
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@danifranco
danifranco committed Jan 8, 2024
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83 changes: 55 additions & 28 deletions biapy/config/config.py
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Expand Up @@ -788,49 +788,76 @@ def __init__(self, job_dir, job_identifier):
# Apply a binary mask to remove possible segmentation outside it
_C.TEST.POST_PROCESSING.APPLY_MASK = False

# Remove instances by the conditions based in each instance properties. The three variables, i.e. TEST.POST_PROCESSING.REMOVE_BY_PROPERTIES,
# TEST.POST_PROCESSING.REMOVE_BY_PROPERTIES_VALUES and TEST.POST_PROCESSING.REMOVE_BY_PROPERTIES_SIGN will compose a list
# of conditions to remove the instances. They are list of list of conditions. For instance, the conditions can be
# like this: [['A'], ['B','C']]. Then, if the instance satisfy the first list of conditions, only 'A' in this first case
# (from ['A'] list), or satisfy 'B' and 'C' (from ['B','C'] list) it will be removed from the image. In each sublist all the conditions
# must be satisfied. Available properties are: ['circularity', 'npixels', 'area', 'diameter']. When this post-processing step is
# selected two .csv files will be created, one with the properties of each instance from the original image (will be placed in
# PATHS.RESULT_DIR.PER_IMAGE_INSTANCES path), and another with only instances that remain once this post-processing has been
# applied (will be placed in PATHS.RESULT_DIR.PER_IMAGE_POST_PROCESSING path). In those csv files two more information columns will
# appear: a list of conditions that each instance has satisfy or not ('Satisfied', 'No satisfied' respectively), and a comment with two
# possible values, 'Strange' and 'Correct', telling you if the instance has been removed or not, respectively.
# Whether to measure morphological features on each instances. It is always done
_C.TEST.POST_PROCESSING.MEASURE_PROPERTIES = CN()
_C.TEST.POST_PROCESSING.MEASURE_PROPERTIES.ENABLE = False
# Remove instances by the conditions based in each instance properties. The three variables, i.e.
# TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES.PROPS, TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES.VALUES
# and TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES.SIGN will compose a list of conditions to remove the instances.
# They are list of list of conditions. For instance, the conditions can be like this: [['A'], ['B','C']]. Then, if the instance satisfy
# the first list of conditions, only 'A' in this first case (from ['A'] list), or satisfy 'B' and 'C' (from ['B','C'] list) it will be
# removed from the image. In each sublist all the conditions must be satisfied. Available properties are: ['circularity', 'elongation',
# 'npixels', 'area', 'diameter', 'perimeter', 'sphericity']. When this post-processing step is selected two .csv files
# will be created, one with the properties of each instance from the original image (will be placed in PATHS.RESULT_DIR.PER_IMAGE_INSTANCES
# path), and another with only instances that remain once this post-processing has been applied (will be placed in
# PATHS.RESULT_DIR.PER_IMAGE_POST_PROCESSING path). In those csv files two more information columns will appear: a list of conditions
# that each instance has satisfy or not ('Satisfied', 'No satisfied' respectively), and a comment with two possible values, 'Removed'
# and 'Correct', telling you if the instance has been removed or not, respectively. Some of the properties follow the formulas used in
# MorphoLibJ library for Fiji https://doi.org/10.1093/bioinformatics/btw413
#
# Each property descrition:
# * 'circularity' of an area instance indicates how compact or elongated the instance is. A value of 1 indicates that the instance
# is a perfect circle (compact), and 0 indicates that it is a line (elongated). Calculated as: (4 * PI * area) / (perimeter^2).
# In 3D, circularity is only measured in the center slices of the instance (one slice before the central slice, the central
# slice, and one after it).
# * 'circularity' is defined as the ratio of area over the square of the perimeter, normalized such that the value for a disk equals
# one: (4 * PI * area) / (perimeter^2). Only measurable for 2D images (use sphericity for 3D images). While values of circularity
# range theoretically within the interval [0;1], the measurements errors of the perimeter may produce circularity values above 1
# (Lehmann et al., 201211 ; https://doi.org/10.1093/bioinformatics/btw413).
#
# * 'elongation' is the inverse of the circularity. The values of elongation range from 1 for round particles and increase for
# elongated particles. Calculated as: (perimeter^2)/(4 * PI * area) . Only measurable for 2D images.
#
# * 'npixels' corresponds to the sum of pixels that compose an instance.
#
# * 'area' correspond to the number of pixels taking into account the image resolution (we call it 'area' also even in a 3D
# image for simplicity, but that will be the volume in that case). In the resulting statistics 'volume' will appear in that
# case too.
# * 'npixels' corresponds to the sum of pixels that compose an instance.
#
# * 'diameter' calculated with the bounding box and by taking the maximum value of the box in x and y axes. In 3D, z axis
# is also taken into account. Does not take into account the image resolution.
_C.TEST.POST_PROCESSING.REMOVE_BY_PROPERTIES = []
# Values of the properties listed in TEST.POST_PROCESSING.REMOVE_BY_PROPERTIES that the instances need to satisfy to
# not be droped. Values for each property:
# is also taken into account. Does not take into account the image resolution.
#
# * 'perimeter', in 2D, approximates the contour as a line through the centers of border pixels using a 4-connectivity. In 3D,
# it is the surface area computed using Lewiner et al. algorithm (https://www.tandfonline.com/doi/abs/10.1080/10867651.2003.10487582)
# using marching_cubes (https://scikit-image.org/docs/stable/api/skimage.measure.html#skimage.measure.marching_cubes) and
# mesh_surface_area(https://scikit-image.org/docs/stable/api/skimage.measure.html#skimage.measure.mesh_surface_area) functions
# of scikit-image.
#
# * 'sphericity', in 3D, it is the ratio of the squared volume over the cube of the surface area, normalized such that the value
# for a ball equals one: (36 * PI)*((volume^2)/(perimeter^3)). Only measurable for 3D images (use circularity for 2D images).
#
_C.TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES = CN()
# Whether to enable or not the filtering by properties
_C.TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES.ENABLE = False
# List of properties to apply a filter. Available properties are: ['circularity', 'elongation', 'npixels', 'area', 'diameter',
# 'perimeter', 'sphericity']
_C.TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES.PROPS = []
# Values of the properties listed in TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES that the instances need to
# satisfy to not be droped. Values for each property:
# * 'circularity' must be a float in [0,1] range.
# * 'area', 'npixels' and 'diameter' can be integer/float.
_C.TEST.POST_PROCESSING.REMOVE_BY_PROPERTIES_VALUES = []
_C.TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES.VALUES = []
# Sign to do the comparison. Options: ['gt', 'ge', 'lt', 'le'] that corresponds to "greather than", e.g. ">",
# "greather equal", e.g. ">=", "less than", e.g. "<", and "less equal" e.g. "<=" comparisons.
_C.TEST.POST_PROCESSING.REMOVE_BY_PROPERTIES_SIGN = []
_C.TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES.SIGN = []
# A full example of this post-processing:
# If you want to remove those instances that have less than 100 pixels and circularity less equal to 0.7 you should
# declare the above three variables as follows:
# _C.TEST.POST_PROCESSING.REMOVE_BY_PROPERTIES = [['npixels', 'circularity']]
# _C.TEST.POST_PROCESSING.REMOVE_BY_PROPERTIES_VALUES = [[100, 0.7]]
# _C.TEST.POST_PROCESSING.REMOVE_BY_PROPERTIES_SIGN = [['lt', 'le']]
# _C.TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES = [['npixels', 'circularity']]
# _C.TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES_VALUES = [[100, 0.7]]
# _C.TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES_SIGN = [['lt', 'le']]
# You can also concatenate more restrictions and they will be applied in order. For instance, if you want to remove those
# instances that are bigger than an specific area, and do that before the condition described above, you can define the
# variables this way:
# _C.TEST.POST_PROCESSING.REMOVE_BY_PROPERTIES = [['area'], ['npixels', 'circularity']]
# _C.TEST.POST_PROCESSING.REMOVE_BY_PROPERTIES_VALUES = [[500], [100, 0.7]]
# _C.TEST.POST_PROCESSING.REMOVE_BY_PROPERTIES_SIGN = [['gt'], ['lt', 'le']]
# _C.TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES = [['area'], ['npixels', 'circularity']]
# _C.TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES_VALUES = [[500], [100, 0.7]]
# _C.TEST.POST_PROCESSING.MEASURE_PROPERTIES.REMOVE_BY_PROPERTIES_SIGN = [['gt'], ['lt', 'le']]
# This way, the instances will be removed by 'area' and then by 'npixels' and 'circularity'

### Instance segmentation
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