def test_trainin_kidney_from_pklz(self):
sample_data_path = lisa.dataset.join_sdp("kidney_training/")
test_data = lisa.dataset.join_sdp(
"kidney_training/liver-orig001.mhd.pklz")
label = "left kidney"
organ_model.train_organ_model_from_dir("left_kidney.ol.p",
reference_dir=sample_data_path,
orig_pattern="*orig*.pklz",
ref_pattern="*orig*.pklz",
label=label,
segmentation_key=True)
ol = organ_localizator.OrganLocalizator()
ol.load("left_kidney.ol.p")
dr = io3d.DataReader()
data3d, metadata = dr.Get3DData(test_data)
out = ol.predict(data3d, metadata['voxelsize_mm'])
seg = (metadata["segmentation"] == metadata['slab'][label]).astype(
np.int8)
err = np.sum(np.abs(seg - out))
import sed3
sed3.show_slices(out - seg, out - seg, slice_step=20)
# self.assertEqual(True, False)
# less then 10% error expected
self.assertGreater(np.prod(seg.shape) * 0.1, err)
def test_training(self):
sample_data_path = lisa.dataset.sample_data_path()
organ_localizator.train_liver_localizator_from_sliver_data(
"liver.ol.p",
sliver_reference_dir=sample_data_path,
orig_pattern="*orig*001.mhd",
ref_pattern="*seg*001.mhd",
)
ol = organ_localizator.OrganLocalizator()
ol.load("liver.ol.p")
dr = io3d.DataReader()
data3d, metadata = dr.Get3DData(
op.join(sample_data_path, 'liver-orig001.mhd'))
out = ol.predict(data3d, metadata['voxelsize_mm'])
seg, metadata = dr.Get3DData(
op.join(sample_data_path, 'liver-seg001.mhd'))
err = np.sum(np.abs(seg - out))
# import sed3
# sed3.show_slices(out-seg, out-seg, slice_step=20)
# self.assertEqual(True, False)
# less then 10% error expected
self.assertGreater(np.prod(seg.shape) * 0.1, err)
def near_blur_intensity_localization_fv(data3dr,
voxelsize_mm,
seeds=None,
unique_cls=None): # scale
"""
Use organ_localizator features plus intensity features
:param data3dr:
:param voxelsize_mm:
:param seeds:
:param unique_cls:
:return:
"""
import scipy
import numpy as np
import os.path as op
try:
from lisa import organ_localizator
except:
import organ_localizator
# print "po importech"
fv = []
f0 = scipy.ndimage.filters.gaussian_filter(data3dr,
sigma=0.5).reshape(-1, 1)
f1 = scipy.ndimage.filters.gaussian_filter(data3dr, sigma=3).reshape(-1, 1)
#f2 = scipy.ndimage.filters.gaussian_filter(data3dr, sigma=5).reshape(-1, 1) - f0
#f3 = scipy.ndimage.filters.gaussian_filter(data3dr, sigma=10).reshape(-1, 1) - f0
#f4 = scipy.ndimage.filters.gaussian_filter(data3dr, sigma=20).reshape(-1, 1) - f0
# position
#ss = lisa.body_navigation.BodyNavigation(data3dr, voxelsize_mm)
#ss.feature_function(data3d, voxelsize_mm)
#fd1 = ss.dist_to_lungs().reshape(-1, 1)
#fd2 = ss.dist_to_spine().reshape(-1, 1)
#fd3 = ss.dist_sagittal().reshape(-1, 1)
#fd4 = ss.dist_coronal().reshape(-1, 1)
#fd5 = ss.dist_to_surface().reshape(-1, 1)
#fd6 = ss.dist_diaphragm().reshape(-1, 1)
# print "pred f6"
f6 = scipy.ndimage.filters.gaussian_filter(
data3dr, sigma=[20, 1, 1]).reshape(-1, 1) - f1
f7 = scipy.ndimage.filters.gaussian_filter(
data3dr, sigma=[1, 20, 1]).reshape(-1, 1) - f1
f8 = scipy.ndimage.filters.gaussian_filter(
data3dr, sigma=[1, 1, 20]).reshape(-1, 1) - f1
# print "pred organ_localizator"
ol = organ_localizator.OrganLocalizator()
ol.load(op.expanduser("~/lisa_data/liver.ol.p"))
fdall = ol.feature_function(data3dr, voxelsize_mm)
middle_liver = ol.predict_w(data3dr, voxelsize_mm, 0.85)
mn = np.median(data3dr[middle_liver == 1])
fdn = np.ones(f0.shape) * mn
# print "fv shapes ", f0.shape, fd2.shape, fd3.shape
fv = np.concatenate(
[
f0,
f1,
# f2, f3, f4,
# fd1, fd2, fd3, fd4, fd5, fd6,
fdall,
f6,
f7,
f8,
fdn,
],
1)
if seeds is not None:
# logger.debug("seeds " + str(seeds))
# print "seeds ", seeds
sd = seeds.reshape(-1, 1)
selection = np.in1d(sd, unique_cls)
fv = fv[selection]
sd = sd[selection]
# sd = sd[]
return fv, sd
return fv