Source code for niftynet.contrib.evaluation.segmentation_evaluations

This module holds built-in segmentation evaluations without tests

import os

import numpy as np
import pandas as pd
from scipy import ndimage

from niftynet.evaluation.base_evaluations import BaseEvaluation
from niftynet.evaluation.segmentation_evaluations import \
    PerComponentEvaluation, connected_components, cached_label_binarizer, \
from import save_data_array

[docs]class com_ref(PerComponentEvaluation): """ Computes the centers of mass of each component in the reference standard """
[docs] def metric_from_binarized(self, seg, ref): """ :param seg: numpy array with binary mask from inferred segmentation :param ref: numpy array with binary mask from reference segmentation :return: dict of centers of mass in each axis """ return {d: 'com_ref_' + x for d, x in zip('XYZ', ndimage.center_of_mass(ref))}
[docs]class ErrorMapsCC(BaseEvaluation): """ Create 3 maps of connected component detection: tpc_map shows each detected ref cc (having at least on seg cc that overlaps) and the union of all overlapping seg ccs fnc_map shows all ref ccs that were not detected fpc_map shows all seg ccs that did not overlap any ref ccs Note we currently arbitrarily limit image generation to binary problems """
[docs] def layer_op(self, subject_id, data): analyses = self.app_param.evaluation_units.split(',') if 'label' not in analyses and 'foreground' not in analyses: raise ValueError('ErrorMaps work with label or foreground ' 'analyses only') if self.app_param.num_classes > 2: raise ValueError('ErrorMaps work with binary segmentations only') binarizer = cached_label_binarizer(1, self.app_param.output_prob) seg, ref = binarizer(data) cc_func = connected_components cc_seg, cc_ref = cc_func(seg, ref, self.app_param.output_prob) cc_aggregator = union_of_seg_for_each_ref_cc ccs = cc_aggregator(cc_ref, cc_seg) tp_seg_labels = set(s for seg_l, ref_l in ccs for s in seg_l) tp_ref_labels = set(r for seg_l, ref_l in ccs for r in ref_l if len( seg_l)) fn_ref_labels = set(range(1, cc_ref[1])) - tp_ref_labels fp_seg_labels = set(range(1, cc_seg[1])) - tp_seg_labels maps = {} maps['tpc_map'] = np.logical_or(cc_seg in tp_seg_labels, cc_ref in tp_ref_labels) maps['fnc_map'] = cc_ref in fn_ref_labels maps['fpc_map'] = cc_seg in fp_seg_labels image_idx = self.reader.get_image_index(subject_id) file_path = os.path.join(self.eval_param.save_csv_dir, 'images') out = {'subject_id': subject_id} for key in maps: out[key] = os.path.join(file_path, subject_id + '_' + key + '.nii') save_data_array(file_path, subject_id + '_' + key + '.nii', maps[key], self.reader.output_list[image_idx]['label'], 0) pdf = pd.DataFrame.from_records([out], ('subject_id',)) return [pdf]