import os
import unittest
import warnings
import numpy as np
from pydicom.tag import Tag
from dosma.core.med_volume import MedicalVolume
from dosma.core.quant_vals import QuantitativeValue
from dosma.models.util import get_model
from dosma.scan_sequences.mri.qdess import QDess
from dosma.tissues.femoral_cartilage import FemoralCartilage
import keras.backend as K
from ... import util
SEGMENTATION_WEIGHTS_FOLDER = os.path.join(
os.path.dirname(__file__), "../../../weights/iwoai-2019-t6-normalized"
)
SEGMENTATION_MODEL = "iwoai-2019-t6-normalized"
class QDessTest(util.ScanTest):
SCAN_TYPE = QDess
def _generate_mock_data(self, shape=None, metadata=True):
"""Generates arbitrary mock data for QDess sequence.
Metadata values were extracted from a real qDESS sequence.
"""
if shape is None:
shape = (10, 10, 10)
e1 = MedicalVolume(np.random.rand(*shape) * 80 + 0.1, affine=np.eye(4))
e2 = MedicalVolume(np.random.rand(*shape) * 40 + 0.1, affine=np.eye(4))
ys = [e1, e2]
ts = [8, 42]
if metadata:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for idx, (y, t) in enumerate(zip(ys, ts)):
y.set_metadata("EchoTime", t, force=True)
y.set_metadata("EchoNumber", idx + 1, force=True)
y.set_metadata("RepetitionTime", 25.0, force=True)
y.set_metadata("FlipAngle", 30.0, force=True)
y.set_metadata(Tag(0x001910B6), 3132.0, force=True) # gradient time
y.set_metadata(Tag(0x001910B7), 1560.0, force=True) # gradient area
return ys, ts, None
def test_basic(self):
ys, _, _ = self._generate_mock_data()
scan = QDess(ys)
assert scan.ref_dicom == ys[0].headers(flatten=True)[0]
with self.assertRaises(ValueError):
_ = QDess(ys + ys)
def test_calc_rss(self):
ys, _, _ = self._generate_mock_data()
scan = QDess(ys)
rss = scan.calc_rss()
assert np.allclose(rss.A, np.sqrt(ys[0] ** 2 + ys[1] ** 2).A)
def test_generate_t2_map(self):
ys, _, _ = self._generate_mock_data()
scan = QDess(ys)
tissue = FemoralCartilage()
t2 = scan.generate_t2_map(tissue)
assert isinstance(t2, QuantitativeValue)
def test_save_load(self):
ys, _, _ = self._generate_mock_data()
scan = QDess(ys)
save_dir = os.path.join(self.data_dirpath, "test_save_load")
pik_file = scan.save(save_dir, save_custom=True)
assert os.path.isfile(pik_file)
assert all(
os.path.isfile(os.path.join(save_dir, "volumes", f"echo-{idx:03d}.nii.gz"))
for idx in range(2)
)
scan2 = QDess.load(pik_file)
for v1, v2 in zip(scan.volumes, scan2.volumes):
assert v1.is_identical(v2)
@unittest.skipIf(not util.is_data_available(), "unittest data is not available")
def test_segmentation_multiclass(self):
"""Test support for multiclass segmentation."""
scan = self.SCAN_TYPE.from_dicom(self.dicom_dirpath, num_workers=util.num_workers())
tissue = FemoralCartilage()
tissue.find_weights(SEGMENTATION_WEIGHTS_FOLDER),
dims = scan.get_dimensions()
input_shape = (dims[0], dims[1], 1)
model = get_model(
SEGMENTATION_MODEL, input_shape=input_shape, weights_path=tissue.weights_file_path
)
scan.segment(model, tissue, use_rss=True)
# This should call __del__ in KerasSegModel
model = None
K.clear_session()
@unittest.skipIf(not util.is_data_available(), "unittest data is not available")
def test_cmd_line(self):
# Generate segmentation mask for femoral cartilage via command line.
cmdline_str = (
f"--d {self.dicom_dirpath} --s {self.data_dirpath} qdess --fc "
f"segment --weights_dir {SEGMENTATION_WEIGHTS_FOLDER} "
f"--model {SEGMENTATION_MODEL} --use_rss"
)
self.__cmd_line_helper__(cmdline_str)
# Generate T2 map for femoral cartilage, tibial cartilage, and meniscus.
cmdline_str = (
f"--l {self.data_dirpath} qdess --fc t2 --suppress_fat " f"--suppress_fluid --beta 1.1"
)
self.__cmd_line_helper__(cmdline_str)
if __name__ == "__main__":
unittest.main()
Datasets
Models
