Source code for nighres.statistics.segmentation_statistics

import numpy as np
import nibabel as nb
import os
import sys
import nighresjava
from import load_volume, save_volume
from ..utils import _output_dir_4saving, _fname_4saving, \
                    _check_topology_lut_dir, _check_available_memory

[docs]def segmentation_statistics(segmentation, intensity=None, template=None, statistics=None, output_csv=None, atlas=None, skip_first=True, ignore_zero=True, save_data=False, overwrite=False, output_dir=None, file_name=None): """ Segmentation Statistics Compute various statistics of image segmentations Parameters ---------- segmentation: niimg Input segmentation image intensity: niimg, optional Input intensity image for intensity-based statistics template: niimg, optional Input template segmentation for comparisons statistics: {"Voxels", "Volume", "Mean_intensity", "Std_intensity", "10_intensity", "25_intensity", "50_intensity", "75_intensity", "90_intensity", "Volumes", "Dice_overlap", "Jaccard_overlap", "Volume_difference", "False_positives", "False_negatives", "Dilated_Dice_overlap", "Dilated_false_positive", "Dilated_false_negative", "Dilated_false_negative_volume", "Dilated_false_positive_volume", "Detected_clusters", "False_detections", "Cluster_numbers", "Mean_cluster_sizes", "Cluster_maps", "Average_surface_distance", "Average_surface_difference", "Average_squared_surface_distance", "Hausdorff_distance"} Statistics to compute output_csv: str, optional File name of the statistics file to generate or expand atlas: str, optional File name of an atlas file defining the segmentation labels skip_first: bool, optional Whether to skip the first segmentation label (usually representing the background, default is True) ignore_zero: bool, optional Whether to ignore zero intensity values in the intensity image (default is True) save_data: bool, optional Save output data to file (default is False) overwrite: bool, optional Overwrite existing results (default is False) output_dir: str, optional Path to desired output directory, will be created if it doesn't exist file_name: str, optional Desired base name for output files with file extension (suffixes will be added) Returns ---------- dict Dictionary collecting outputs under the following keys (suffix of output files in brackets) * csv (str): The csv statistics file * map (niimg): Map of the estimated statistic, if relevant (stat-map) Notes ---------- Original Java module by Pierre-Louis Bazin. """ print('\nSegmentation statistics') # make sure that saving related parameters are correct if save_data: output_dir = _output_dir_4saving(output_dir,segmentation) map_file = os.path.join(output_dir, _fname_4saving(file_name=file_name, rootfile=segmentation, suffix='stat-map')) csv_file = os.path.join(output_dir, output_csv) if overwrite is False \ and os.path.isfile(csv_file) : # check that the denoised data is the same too print("append results to existing csv file") if overwrite is True: # delete current stats file to start from the beginning os.remove(csv_file) else: csv_file = output_csv # start virtual machine, if not already running try: mem = _check_available_memory() nighresjava.initVM(initialheap=mem['init'], maxheap=mem['max']) except ValueError: pass # create algorithm instance stats = nighresjava.StatisticsSegmentation() # load first image and use it to set dimensions and resolution img = load_volume(segmentation) data = img.get_data() affine = img.affine header = img.header resolution = [x.item() for x in header.get_zooms()] dimensions = data.shape stats.setDimensions(dimensions[0], dimensions[1], dimensions[2]) stats.setResolutions(resolution[0], resolution[1], resolution[2]) stats.setSegmentationImage(nighresjava.JArray('int')( (data.flatten('F')).astype(int).tolist())) stats.setSegmentationName(_fname_4saving(rootfile=segmentation)) # other input images, if any if intensity is not None: data = load_volume(intensity).get_data() stats.setIntensityImage(nighresjava.JArray('float')( (data.flatten('F')).astype(float))) stats.setIntensityName(_fname_4saving(rootfile=intensity)) if template is not None: data = load_volume(template).get_data() stats.setTemplateImage(nighresjava.JArray('int')( (data.flatten('F')).astype(int).tolist())) stats.setTemplateName(_fname_4saving(rootfile=template)) # set algorithm parameters if atlas is not None: stats.setAtlasFile(atlas) stats.setSkipFirstLabel(skip_first) stats.setIgnoreZeroIntensities(ignore_zero) if len(statistics)>0: stats.setStatistic1(statistics[0]) if len(statistics)>1: stats.setStatistic2(statistics[1]) if len(statistics)>2: stats.setStatistic3(statistics[2]) stats.setSpreadsheetFile(csv_file) # execute the algorithm try: stats.execute() except: # if the Java module fails, reraise the error it throws print("\n The underlying Java code did not execute cleanly: ") print(sys.exc_info()[0]) raise return # reshape output to what nibabel likes output = False for st in statistics: if st=="Cluster_maps": output=True if (output): data = np.reshape(np.array(stats.getOutputImage(), dtype=np.int32), dimensions, 'F') header['cal_min'] = np.nanmin(data) header['cal_max'] = np.nanmax(data) output = nb.Nifti1Image(data, affine, header) if save_data: save_volume(map_file, output) csv_file = stats.getOutputFile() if output: return {'csv': csv_file, 'map': output} else: return {'csv': csv_file}