# Microscope profile settings
# Author: David Young, 2020
"""Profile settings for processing regions of interests.
These setting typically involve high-resolution such as microscopy images.
"""
from typing import Dict, Optional, Tuple
from magmap.settings import config
from magmap.settings import profiles
[docs]
class ROIProfile(profiles.SettingsDict):
"""ROI profile dictionary.
Attributes:
PATH_PREFIX (str): Prefix for ROI profile files.
BLOB_PREPROCESSING (tuple): Keys for blob preprocessing.
BLOCK_SIZES (tuple): Keys for block process sizing.
"""
PATH_PREFIX = "roi"
BLOB_PREPROCESSING = (
"clip_vmin",
"clip_vmax",
"clip_min",
"clip_max",
"max_thresh_factor",
"tot_var_denoise",
"unsharp_strength",
"erosion_threshold",
"adapt_hist_lim",
)
BLOCK_SIZES = (
"segment_size",
"denoise_size",
"prune_tol_factor",
"sub_stack_max_pixels",
"isotropic",
)
def __init__(self, *args, **kwargs):
"""Initialize an ROI profile dictionary.
Args:
*args:
**kwargs:
"""
super().__init__(self)
self[self.NAME_KEY] = self.DEFAULT_NAME
# visualization and plotting
self["vis_3d"] = "points" # "points" or "surface" 3D visualization
self["points_3d_thresh"] = 0.85 # frac of thresh (changed in v.0.6.6)
self["channel_colors"] = (
config.Cmaps.CMAP_GRBK_NAME,
config.Cmaps.CMAP_RDBK_NAME,
config.Cmaps.CMAP_BUBK_NAME,
config.Cmaps.CMAP_YLBK_NAME,
config.Cmaps.CMAP_MGBK_NAME,
config.Cmaps.CMAP_CYBK_NAME,
)
self["scale_bar_color"] = "w"
#: Colorbar args passed to :meth:`matplotlib.figure.Figure.colorbar`.
self["colorbar"] = None
# num of times to rotate image by 90deg after loading
self["load_rot90"] = 0
self["norm"] = None # (min, max) normalization of image5d
# image preprocessing before blob detection
self["clip_vmin"] = 5 # vmin/vmax set contrast stretching, range 0-100
self["clip_vmax"] = 99.5
self["clip_min"] = 0.2 # min/max clip after stretching, range 0-1
self["clip_max"] = 1.0
# config.near_max multiplier for global max threshold; lower to inc
# sensitivity; need to dec blob sizes if inc
self["max_thresh_factor"] = 0.5
self["tot_var_denoise"] = None # weight (eg skimage default 0.1)
self["unsharp_strength"] = 0.3 # unsharp filter (sharpens images)
self["erosion_threshold"] = 0.2 # erode clumped cells
# adaptive histogram equalization clipping limit
self["adapt_hist_lim"] = 0.1
# 3D blob detection settings
self["min_sigma_factor"] = 3
self["max_sigma_factor"] = 5
self["num_sigma"] = 10
self["detection_threshold"] = 0.1
self["overlap"] = 0.5
self["thresholding"] = None
self["thresholding_size"] = -1
# z,y,x px to exclude along border after blob detection
self["exclude_border"] = None
# BLOCK PROCESSING AND AUTOMATED VERIFICATION
# Whole image processing occurs in blocks to parallel processing
# and decreased memory utilization. If block settings are identical,
# the same blocks will be used for all channels (see
# is_identical_block_settings below for settings checked).
# multiprocessing start method; if method not available for the given
# platform, the default method for the platform will be used instead
self["mp_start"] = "fork" # fork, spawn, or forkserver
# max tasks per child process; use smaller integers (eg 1) to replace
# worker processes and free their resources after fewer tasks
self["mp_max_tasks"] = None # does not replace workers
self["segment_size"] = 500 # detection ROI max size along longest edge
# max size along longest edge for denoising blocks within
# segmentation blobs; None turns off preprocessing in stack proc;
# make much larger than segment_size (eg 2x) to cover the full segment
# ROI because of additional overlap in segment ROIs
self["denoise_size"] = 25
# z,y,x tolerances for pruning duplicates in overlapped regions
self["prune_tol_factor"] = (1, 1, 1)
self["verify_tol_factor"] = (1, 1, 1)
# module level variable will take precedence
self["sub_stack_max_pixels"] = (1000, 1000, 1000)
# anisotropic resizing:
# setting an isotropic factor automatically calculates isotropy
# based on the image resolutions, and the values given here are
# scaling factors in z,y,x applied after isotropic scaling;
# eg (0.7, 1, 1) rescales the z-axis to be 0.7x isotropic;
# None turns off any isotropic rescaling
self["isotropic"] = None # scale ROI for blob detection
self["isotropic_vis"] = (1, 1, 1) # only for visualization
self["resize_blobs"] = None # z,y,x coord scaling before verification
#: Spectral unmixing for channel subtraction.
#: Provide as: ``{channel: {channel_to_subtract: factor, ...}, ...}``.
#: Eg, to subtract 90% of channel 1 from 3: ``{3: [1, 0.9]}``.
self.spectral_unmixing: Optional[Dict[int, Tuple[int, float]]] = None
# update with args
self.update(*args, **kwargs)
self.profiles = {
# Lightsheet nuclei
# pre-v01
# v1 (MagellanMapper v0.6.1)
# v2 (MagellanMapper v0.6.2): isotropy (no anisotropic detection), dec
# clip_max, use default sub_stack_max_pixels
# v2.1 (MagellanMapper v0.6.4): erosion_threshold
# v2.2 (MagellanMapper v0.6.6): narrower and taller stack shape
# v2.3 (MagellanMapper v0.8.7): added prune_tol_factor
# v2.4 (MagellanMapper v0.8.8): decreased min/max sigma, segment size
# v2.5 (MagellanMapper v0.8.9): added exclude_border
# v2.6 (MagellanMapper v0.9.3): slight dec in x/y verify tol for
# Hungarian method
# v2.6.1 (MagellanMapper v0.9.4): scale_factor, segmenting_mean_thresh
# had already been turned off and now removed completely
"lightsheet": {
"points_3d_thresh": 0.7,
"clip_vmax": 98.5,
"clip_min": 0,
"clip_max": 0.5,
"unsharp_strength": 0.3,
"erosion_threshold": 0.3,
"min_sigma_factor": 2.6,
"max_sigma_factor": 2.8,
"num_sigma": 10,
"overlap": 0.55,
"segment_size": 150,
"prune_tol_factor": (1, 0.9, 0.9),
"verify_tol_factor": (3, 1.2, 1.2),
"isotropic": (0.96, 1, 1),
"isotropic_vis": (0.5, 1, 1),
"sub_stack_max_pixels": (1200, 800, 800),
"exclude_border": (1, 0, 0),
},
# minimal preprocessing
"minpreproc": {
"clip_vmin": 0,
"clip_vmax": 99.99,
"clip_max": 1,
"tot_var_denoise": 0.01,
"unsharp_strength": 0,
"erosion_threshold": 0,
},
# low resolution
"lowres": {
"min_sigma_factor": 10,
"max_sigma_factor": 14,
"isotropic": None,
"denoise_size": 2000, # will use full detection ROI
"segment_size": 1000,
"max_thresh_factor": 1.5,
"exclude_border": (8, 1, 1),
"verify_tol_factor": (3, 2, 2),
},
# 2-photon 20x nuclei
"2p20x": {
"vis_3d": "surface",
"clip_vmax": 97,
"clip_min": 0,
"clip_max": 0.7,
"tot_var_denoise": True,
"unsharp_strength": 2.5,
# smaller threshold since total var denoising
# "points_3d_thresh": 1.1
"min_sigma_factor": 2.6,
"max_sigma_factor": 4,
"num_sigma": 20,
"overlap": 0.1,
"thresholding": None, # "otsu"
# "thresholding_size": 41,
"thresholding_size": 64, # for otsu
# "thresholding_size": 50.0, # for random_walker
"denoise_size": 25,
"segment_size": 100,
"prune_tol_factor": (1.5, 1.3, 1.3),
},
# 2p 20x of zebrafish nuclei
"zebrafish": {
"min_sigma_factor": 2.5,
"max_sigma_factor": 3,
},
# higher contrast colormaps
"contrast": {
"channel_colors": ("inferno", "inferno"),
"scale_bar_color": "w",
},
# similar colormaps to greyscale but with a cool blue tinge
"bone": {
"channel_colors": ("bone", "bone"),
"scale_bar_color": "w",
},
# diverging colormaps for heat maps centered on 0
"diverging": {
"channel_colors": ("RdBu", "BrBG"),
"scale_bar_color": "k",
"colorbar": {"shrink": 0.7},
},
# lightsheet 5x of cytoplasmic markers
"cytoplasm": {
"clip_min": 0.3,
"clip_max": 0.8,
"points_3d_thresh": 0.7,
# adjust sigmas based on extent of cyto staining;
# TODO: consider adding sigma_mult if ratio remains
# relatively const
"min_sigma_factor": 4,
"max_sigma_factor": 10,
"num_sigma": 10,
"overlap": 0.2,
},
# isotropic image that does not require interpolating visually
"isotropic": {
"points_3d_thresh": 0.3, # used only if not surface
"isotropic_vis": (1, 1, 1),
},
# binary image
"binary": {
"denoise_size": None,
"detection_threshold": 0.001,
},
# adjust nuclei size for 4x magnification
"4xnuc": {
"min_sigma_factor": 3,
"max_sigma_factor": 4,
},
# fit into ~32GB RAM instance after isotropic interpolation
"20x": {
"segment_size": 50,
},
# export to deep learning framework with required dimensions
"exportdl": {
"isotropic": (0.93, 1, 1),
},
# downsample an image previously upsampled for isotropy
"downiso": {
"isotropic": None, # assume already isotropic
"resize_blobs": (.2, 1, 1),
},
# rotate by 180 deg
# TODO: replace with plot labels config setting?
"rot180": {
"load_rot90": 2, # rotation by 180deg
},
# denoise settings when performing registration
"register": {
"unsharp_strength": 1.5,
},
# color and intensity geared toward histology atlas images
"atlas": {
"channel_colors": ("gray",),
"clip_vmax": 97,
},
# colors for each channel based on randomly generated discrete colormaps
"randomcolors": {
"channel_colors": [],
},
# normalize image5d and associated metadata to intensity values
# between 0 and 1
"norm": {
"norm": (0.0, 1.0),
},
# multiprocessing with spawn mode
"spawn": {
"mp_start": "spawn",
}
}
[docs]
@staticmethod
def get_files(profiles_dir=None, filename_prefix=None):
"""Get ROI profile files.
Args:
profiles_dir (str): Directory from which to get files; defaults
to None.
filename_prefix (str): Only get files starting with this string;
defaults to None to use :const:`PATH_PREFIX`.
Returns:
List[str]: List of files in ``profiles_dir`` matching the given
``filename_prefix``.
"""
if not filename_prefix:
filename_prefix = ROIProfile.PATH_PREFIX
return super(ROIProfile, ROIProfile).get_files(
profiles_dir, filename_prefix)