def test_04_perfect_overlap_5mm_margin(self):
_, tumor_np = niftireader.load_image(
_get_file_name('T01', '04_perfect_overlap_5mm_margin', 'Tumor'))
_, ablation_np = niftireader.load_image(
_get_file_name('T01', '04_perfect_overlap_5mm_margin', 'Ablation'))
distances = margin.compute_distances(tumor_np, ablation_np, None, 1)
df = margin.summarize_surface_dists('T01', 'L01', distances)
print(df)
record = df.loc[(df['Patient'] == "T01")
& (df['Lesion'] == 'L01')].iloc[0]
self.assertEqual(record["nr_voxels"], 222.0)
self.assertAlmostEqual(record["min_distance"], 4.12, delta=0.01)
self.assertAlmostEqual(record["max_distance"], 5.0, delta=0.01)
def test_03_n5mm_margin_subcapsular(self):
_, tumor_np = niftireader.load_image(
_get_file_name('T02', '03_-5mm_margin_subcapsular', 'Tumor'))
_, ablation_np = niftireader.load_image(
_get_file_name('T02', '03_-5mm_margin_subcapsular', 'Ablation'))
_, liver_np = niftireader.load_image(
_get_file_name('T02', '03_-5mm_margin_subcapsular', 'Liver'))
distances = margin.compute_distances(tumor_np, ablation_np, liver_np,
1)
df = margin.summarize_surface_dists('T02', 'L03', distances)
print(df)
record = df.loc[(df['Patient'] == "T02")
& (df['Lesion'] == 'L03')].iloc[0]
self.assertEqual(record["nr_voxels"], 317.0)
self.assertAlmostEqual(record["min_distance"], -5.09, delta=0.01)
self.assertAlmostEqual(record["max_distance"], -2.23, delta=0.01)
patient_id = 'Test'
if lesion_id is None:
lesion_id = 1
if ablation_date is None:
ablation_date = today.strftime("%d-%m-%Y")
if output_file is None:
csv_dir = 'surface_distances_csv'
if not os.path.exists(csv_dir):
os.mkdir(csv_dir)
output_file = os.path.join(csv_dir, str(patient_id) + '_' + str(lesion_id) + '_surface_distances.xlsx')
if dir_path is None:
dir_path = 'figures'
if not os.path.exists(dir_path):
os.mkdir(dir_path)
tumor, tumor_np = load_image(tumor_file)
# check if there is actually a segmentation in the file
has_tumor_segmented = np.sum(tumor_np.astype(np.uint8)) > 0
if has_tumor_segmented is False:
print('No tumor segmentation mask found in the file provided...program exiting')
sys.exit()
ablation, ablation_np = load_image(ablation_file)
has_ablation_segmented = np.sum(ablation_np.astype(np.uint8)) > 0
if has_ablation_segmented is False:
print('No ablation segmentation mask found in the file provided...program exiting')
sys.exit()
if liver_file is not None:
# load the image file
liver, liver_np = load_image(liver_file)