Source code for AFQ.tasks.segmentation

import nibabel as nib
import os
import os.path as op
from time import time
import numpy as np
import pandas as pd
import logging

import pimms

from AFQ.tasks.decorators import as_file, as_img
from AFQ.tasks.utils import get_fname, with_name, str_to_desc
import AFQ.segmentation as seg
from AFQ.utils.path import drop_extension, write_json
import AFQ.utils.streamlines as aus
from AFQ.tasks.utils import get_default_args
import AFQ.utils.volume as auv

try:
    from trx.io import load as load_trx
    from trx.io import save as save_trx
    from trx.trx_file_memmap import TrxFile
[docs] has_trx = True
except ModuleNotFoundError: has_trx = False from dipy.io.streamline import load_tractogram, save_tractogram from dipy.io.stateful_tractogram import Space, StatefulTractogram from dipy.stats.analysis import afq_profile, gaussian_weights from dipy.tracking.streamline import set_number_of_points, values_from_volume
[docs]logger = logging.getLogger('AFQ')
@pimms.calc("bundles") @as_file('_tractography', include_track=True, include_seg=True)
[docs]def segment(data_imap, mapping_imap, tractography_imap, segmentation_params): """ full path to a trk/trx file containing containing segmented streamlines, labeled by bundle Parameters ---------- segmentation_params : dict, optional The parameters for segmentation. Default: use the default behavior of the seg.Segmentation object. """ bundle_dict = data_imap["bundle_dict"] reg_template = data_imap["reg_template"] streamlines = tractography_imap["streamlines"] if streamlines.endswith(".trk") or streamlines.endswith(".tck"): tg = load_tractogram( streamlines, data_imap["dwi"], Space.VOX, bbox_valid_check=False) is_trx = False elif streamlines.endswith(".trx"): is_trx = True trx = load_trx(streamlines, data_imap["dwi"]) trx.streamlines._data = trx.streamlines._data.astype(np.float32) tg = trx.to_sft() indices_to_remove, _ = tg.remove_invalid_streamlines() if len(indices_to_remove) > 0: logger.warning(f"{len(indices_to_remove)} invalid streamlines removed") start_time = time() segmentation = seg.Segmentation(**segmentation_params) bundles = segmentation.segment( bundle_dict, tg, mapping_imap["mapping"], data_imap["dwi"], reg_template=reg_template) seg_sft = aus.SegmentedSFT(bundles, Space.VOX) if len(seg_sft.sft) < 1: raise ValueError("Fatal: No bundles recognized.") if is_trx: seg_sft.sft.dtype_dict = {'positions': np.float16, 'offsets': np.uint32} tgram = TrxFile.from_sft(seg_sft.sft) tgram.groups = seg_sft.bundle_idxs meta = {} else: tgram, meta = seg_sft.get_sft_and_sidecar() seg_params_out = {} for arg_name, value in segmentation_params.items(): if isinstance(value, (int, float, bool, str)): seg_params_out[arg_name] = value elif isinstance(value, (list, tuple)): seg_params_out[arg_name] = [str(v) for v in value] elif isinstance(value, dict): for k, v in value.items(): seg_params_out[k] = str(v) else: seg_params_out[arg_name] = str(value) meta["source"] = streamlines meta["Recognition Parameters"] = seg_params_out meta["Timing"] = time() - start_time return tgram, meta
@pimms.calc("indiv_bundles")
[docs]def export_bundles(base_fname, results_dir, bundles, tracking_params, segmentation_params): """ dictionary of paths, where each path is a full path to a trk file containing the streamlines of a given bundle. """ is_trx = tracking_params.get("trx", False) if is_trx: extension = ".trx" else: extension = ".trk" bundles_dir = op.join(results_dir, "bundles") os.makedirs(bundles_dir, exist_ok=True) seg_sft = aus.SegmentedSFT.fromfile(bundles) for bundle in seg_sft.bundle_names: if bundle != "whole_brain": fname = op.split( get_fname( base_fname, f'_desc-{str_to_desc(bundle)}' f'_tractography{extension}', tracking_params=tracking_params, segmentation_params=segmentation_params)) fname = op.join(bundles_dir, fname[1]) bundle_sft = seg_sft.get_bundle(bundle) if len(bundle_sft) > 0: logger.info(f"Saving {fname}") if is_trx: seg_sft.sft.dtype_dict = { 'positions': np.float16, 'offsets': np.uint32} trxfile = TrxFile.from_sft(bundle_sft) save_trx(trxfile, fname) else: save_tractogram( bundle_sft, fname, bbox_valid_check=False) else: logger.info(f"No bundle to save for {bundle}") meta = dict(source=bundles) meta_fname = drop_extension(fname) + '.json' write_json(meta_fname, meta) return bundles_dir
@pimms.calc("sl_counts") @as_file('_desc-slCount_dwi.csv', include_track=True, include_seg=True)
[docs]def export_sl_counts(bundles): """ full path to a JSON file containing streamline counts """ sl_counts = [] seg_sft = aus.SegmentedSFT.fromfile(bundles) for bundle in seg_sft.bundle_names: sl_counts.append(len( seg_sft.get_bundle(bundle).streamlines)) sl_counts.append(len(seg_sft.sft.streamlines)) counts_df = pd.DataFrame( data=dict( n_streamlines=sl_counts), index=seg_sft.bundle_names + ["Total Recognized"]) return counts_df, dict(source=bundles)
@pimms.calc("median_bundle_lengths") @as_file( '_desc-medianBundleLengths_dwi.csv', include_track=True, include_seg=True)
[docs]def export_bundle_lengths(bundles): """ full path to a JSON file containing median bundle lengths """ med_len_counts = [] seg_sft = aus.SegmentedSFT.fromfile(bundles) for bundle in seg_sft.bundle_names: these_lengths = seg_sft.get_bundle( bundle)._tractogram._streamlines._lengths if len(these_lengths) > 0: med_len_counts.append(np.median( these_lengths)) else: med_len_counts.append(0) med_len_counts.append(np.median( seg_sft.sft._tractogram._streamlines._lengths)) counts_df = pd.DataFrame( data=dict( median_len=med_len_counts), index=seg_sft.bundle_names + ["Total Recognized"]) return counts_df, dict(source=bundles)
@pimms.calc("density_maps") @as_file('_desc-density_dwi.nii.gz', include_track=True, include_seg=True)
[docs]def export_density_maps(bundles, data_imap): """ full path to 4d nifti file containing streamline counts per voxel per bundle, where the 4th dimension encodes the bundle """ seg_sft = aus.SegmentedSFT.fromfile( bundles) entire_density_map = np.zeros(( *data_imap["data"].shape[:3], len(seg_sft.bundle_names))) for ii, bundle_name in enumerate(seg_sft.bundle_names): bundle_sl = seg_sft.get_bundle(bundle_name) bundle_density = auv.density_map(bundle_sl).get_fdata() entire_density_map[..., ii] = bundle_density return nib.Nifti1Image( entire_density_map, data_imap["dwi_affine"]), dict( source=bundles, bundles=list(seg_sft.bundle_names))
@pimms.calc("profiles") @as_file('_desc-profiles_dwi.csv', include_track=True, include_seg=True)
[docs]def tract_profiles(bundles, scalar_dict, data_imap, profile_weights="gauss", n_points_profile=100): """ full path to a CSV file containing tract profiles Parameters ---------- profile_weights : str, 1D array, 2D array callable, optional How to weight each streamline (1D) or each node (2D) when calculating the tract-profiles. If callable, this is a function that calculates weights. If None, no weighting will be applied. If "gauss", gaussian weights will be used. If "median", the median of values at each node will be used instead of a mean or weighted mean. Default: "gauss" n_points_profile : int, optional Number of points to resample each streamline to before calculating the tract-profiles. Default: 100 """ if not (profile_weights is None or isinstance(profile_weights, str) or callable(profile_weights) or hasattr(profile_weights, "__len__")): raise TypeError( "profile_weights must be string, None, callable, or" + "a 1D or 2D array") if isinstance(profile_weights, str): profile_weights = profile_weights.lower() if isinstance(profile_weights, str) and\ profile_weights != "gauss" and profile_weights != "median": raise TypeError( "if profile_weights is a string," + " it must be 'gauss' or 'median'") bundle_names = [] node_numbers = [] profiles = np.empty((len(scalar_dict), 0)).tolist() this_profile = np.zeros((len(scalar_dict), n_points_profile)) reference = nib.load(scalar_dict[list(scalar_dict.keys())[0]]) seg_sft = aus.SegmentedSFT.fromfile( bundles, reference=reference) seg_sft.sft.to_rasmm() for bundle_name in seg_sft.bundle_names: this_sl = seg_sft.get_bundle(bundle_name).streamlines if len(this_sl) == 0: continue if profile_weights == "gauss": # calculate only once per bundle bundle_profile_weights = gaussian_weights( this_sl, n_points=n_points_profile) for ii, (scalar, scalar_file) in enumerate(scalar_dict.items()): if isinstance(scalar_file, str): scalar_file = nib.load(scalar_file) scalar_data = scalar_file.get_fdata() if isinstance(profile_weights, str): if profile_weights == "gauss": this_prof_weights = bundle_profile_weights elif profile_weights == "median": # weights bundle to only return the mean def _median_weight(bundle): fgarray = set_number_of_points( bundle, n_points_profile) values = np.array( values_from_volume( scalar_data, fgarray, data_imap["dwi_affine"])) weights = np.zeros(values.shape) for ii, jj in enumerate( np.argsort(values, axis=0)[ len(values) // 2, :]): weights[jj, ii] = 1 return weights this_prof_weights = _median_weight else: this_prof_weights = profile_weights this_profile[ii] = afq_profile( scalar_data, this_sl, data_imap["dwi_affine"], weights=this_prof_weights, n_points=n_points_profile) profiles[ii].extend(list(this_profile[ii])) nodes = list(np.arange(this_profile[0].shape[0])) bundle_names.extend([bundle_name] * len(nodes)) node_numbers.extend(nodes) profile_dict = dict() profile_dict["tractID"] = bundle_names profile_dict["nodeID"] = node_numbers for ii, scalar in enumerate(scalar_dict.keys()): profile_dict[scalar] = profiles[ii] profile_dframe = pd.DataFrame(profile_dict) meta = dict(source=bundles, parameters=get_default_args(afq_profile)) return profile_dframe, meta
@pimms.calc("scalar_dict")
[docs]def get_scalar_dict(data_imap, mapping_imap, scalars=["dti_fa", "dti_md"]): """ dicionary mapping scalar names to their respective file paths Parameters ---------- scalars : list of strings and/or scalar definitions, optional List of scalars to use. Can be any of: "dti_fa", "dti_md", "dki_fa", "dki_md", "dki_awf", "dki_mk". Can also be a scalar from AFQ.definitions.image. Default: ["dti_fa", "dti_md"] """ # Note: some scalars preprocessing done in plans, before this step scalar_dict = {} for scalar in scalars: if isinstance(scalar, str): sc = scalar.lower() scalar_dict[sc] = data_imap[f"{sc}"] elif f"{scalar.get_name()}" in mapping_imap: scalar_dict[scalar.get_name()] = mapping_imap[ f"{scalar.get_name()}"] return {"scalar_dict": scalar_dict}
[docs]def get_segmentation_plan(kwargs): if "segmentation_params" in kwargs\ and not isinstance(kwargs["segmentation_params"], dict): raise TypeError( "segmentation_params a dict") segmentation_tasks = with_name([ get_scalar_dict, export_sl_counts, export_bundle_lengths, export_bundles, export_density_maps, segment, tract_profiles]) default_seg_params = get_default_args(seg.Segmentation.__init__) if "segmentation_params" in kwargs: for k in kwargs["segmentation_params"]: default_seg_params[k] = kwargs["segmentation_params"][k] kwargs["segmentation_params"] = default_seg_params return pimms.plan(**segmentation_tasks)