def test_em_3d_demons(): r''' Register a stack of circles ('cylinder') before and after warping them with a synthetic diffeomorphism. This test is intended to detect regressions only: we saved the energy profile (the sequence of energy values at each iteration) of a working version of EM in 3D, and this test checks that the current energy profile matches the saved one. The validation of the "working version" was done by registering the 18 manually annotated T1 brain MRI database IBSR with each other and computing the jaccard index for all 31 common anatomical regions. The "working version" of EM in 3D obtains very similar results as those reported for ANTS on the same database. Any modification that produces a change in the energy profile should be carefully validated to ensure no accuracy loss. ''' moving, static = get_synthetic_warped_circle(30) moving[...,:8] = 0 moving[...,-1:-9:-1] = 0 static[...,:8] = 0 static[...,-1:-9:-1] = 0 #Create the EM metric smooth=25.0 inner_iter=20 step_length=0.25 q_levels=256 double_gradient=True iter_type='demons' similarity_metric = metrics.EMMetric( 3, smooth, inner_iter, q_levels, double_gradient, iter_type) #Create the optimizer level_iters = [10, 5] opt_tol = 1e-4 inv_iter = 20 inv_tol = 1e-3 ss_sigma_factor = 0.5 optimizer = imwarp.SymmetricDiffeomorphicRegistration(similarity_metric, level_iters, step_length, ss_sigma_factor, opt_tol, inv_iter, inv_tol) optimizer.verbosity = VerbosityLevels.DEBUG mapping = optimizer.optimize(static, moving, None) m = optimizer.get_map() assert_equal(mapping, m) energy_profile = np.array(optimizer.full_energy_profile) if floating is np.float32: expected_profile = \ np.array([144.0369470764622, 122.39543007604394, 112.43783718421119, 85.46819602604248, 101.15549228031932, 119.62429965589826, 124.00100190950647, 118.94404608675168, 112.57666071129853, 117.84424645441413, 4470.2719430621, 4138.201850019068, 4007.225585554024, 4074.8853855654797, 3833.6272345908865]) else: expected_profile = \ np.array([144.03695786872666, 121.73922862297613, 107.41132697303448, 90.70731102557508, 97.4295175632117, 112.78404966709469, 103.29910157963684, 111.83865866152108, 121.26265581989485, 118.19913094423933, 4222.003181977351, 4418.042311441615, 4508.671160627819, 4761.251133428944, 4292.8507317299245]) assert_array_almost_equal(energy_profile, expected_profile, decimal=6)
def test_em_3d_demons(): r''' Register a stack of circles ('cylinder') before and after warping them with a synthetic diffeomorphism. This test is intended to detect regressions only: we saved the energy profile (the sequence of energy values at each iteration) of a working version of EM in 3D, and this test checks that the current energy profile matches the saved one. The validation of the "working version" was done by registering the 18 manually annotated T1 brain MRI database IBSR with each other and computing the jaccard index for all 31 common anatomical regions. The "working version" of EM in 3D obtains very similar results as those reported for ANTS on the same database. Any modification that produces a change in the energy profile should be carefully validated to ensure no accuracy loss. ''' moving, static = get_synthetic_warped_circle(30) moving[...,:8] = 0 moving[...,-1:-9:-1] = 0 static[...,:8] = 0 static[...,-1:-9:-1] = 0 #Create the EM metric smooth=25.0 inner_iter=20 step_length=0.25 q_levels=256 double_gradient=True iter_type='demons' similarity_metric = metrics.EMMetric( 3, smooth, inner_iter, q_levels, double_gradient, iter_type) #Create the optimizer level_iters = [10, 5] opt_tol = 1e-4 inv_iter = 20 inv_tol = 1e-3 ss_sigma_factor = 0.5 optimizer = imwarp.SymmetricDiffeomorphicRegistration(similarity_metric, level_iters, step_length, ss_sigma_factor, opt_tol, inv_iter, inv_tol) optimizer.verbosity = VerbosityLevels.DEBUG mapping = optimizer.optimize(static, moving, None) m = optimizer.get_map() assert_equal(mapping, m) energy_profile = subsample_profile( optimizer.full_energy_profile, 10) print(energy_profile) if USING_VC_SSE2: expected_profile = \ np.array([144.03694708, 122.39512307, 111.31925381, 90.9100989, 93.93705232, 104.22993997, 110.57817867, 140.45262039, 133.87804571, 119.20794977]) elif USING_GCC_SSE2: expected_profile = \ np.array([144.03694708, 122.39512227, 111.31924572, 90.91010482, 93.93707059, 104.22996918, 110.57822649, 140.45298465, 133.87831302, 119.20826433]) assert_array_almost_equal(energy_profile, expected_profile, decimal=4)
def test_em_3d_gauss_newton(): r''' Register a stack of circles ('cylinder') before and after warping them with a synthetic diffeomorphism. This test is intended to detect regressions only: we saved the energy profile (the sequence of energy values at each iteration) of a working version of EM in 3D, and this test checks that the current energy profile matches the saved one. The validation of the "working version" was done by registering the 18 manually annotated T1 brain MRI database IBSR with each other and computing the jaccard index for all 31 common anatomical regions. The "working version" of EM in 3D obtains very similar results as those reported for ANTS on the same database. Any modification that produces a change in the energy profile should be carefully validated to ensure no accuracy loss. ''' moving, static = get_synthetic_warped_circle(30) moving[...,:8] = 0 moving[...,-1:-9:-1] = 0 static[...,:8] = 0 static[...,-1:-9:-1] = 0 #Create the EM metric smooth=25.0 inner_iter=20 step_length=0.25 q_levels=256 double_gradient=True iter_type='gauss_newton' similarity_metric = metrics.EMMetric( 3, smooth, inner_iter, q_levels, double_gradient, iter_type) #Create the optimizer level_iters = [10, 5] opt_tol = 1e-4 inv_iter = 20 inv_tol = 1e-3 ss_sigma_factor = 0.5 optimizer = imwarp.SymmetricDiffeomorphicRegistration(similarity_metric, level_iters, step_length, ss_sigma_factor, opt_tol, inv_iter, inv_tol) optimizer.verbosity = VerbosityLevels.DEBUG mapping = optimizer.optimize(static, moving, None) m = optimizer.get_map() assert_equal(mapping, m) energy_profile = subsample_profile( optimizer.full_energy_profile, 10) print(energy_profile) if USING_VC_SSE2: expected_profile = \ np.array([144.03694724, 63.06874155, 51.84694887, 39.6374044, 31.84981429, 44.3778833, 37.84961761, 38.00509734, 38.67423812, 38.47003306]) elif USING_GCC_SSE2: expected_profile = \ np.array([144.03694724, 63.06874148, 51.84694881, 39.63740417, 31.84981481, 44.37788414, 37.84961844, 38.00509881, 38.67423954, 38.47003339]) assert_array_almost_equal(energy_profile, expected_profile, decimal=4)
def test_em_3d_gauss_newton(): r''' Register a stack of circles ('cylinder') before and after warping them with a synthetic diffeomorphism. This test is intended to detect regressions only: we saved the energy profile (the sequence of energy values at each iteration) of a working version of EM in 3D, and this test checks that the current energy profile matches the saved one. The validation of the "working version" was done by registering the 18 manually annotated T1 brain MRI database IBSR with each other and computing the jaccard index for all 31 common anatomical regions. The "working version" of EM in 3D obtains very similar results as those reported for ANTS on the same database. Any modification that produces a change in the energy profile should be carefully validated to ensure no accuracy loss. ''' moving, static = get_synthetic_warped_circle(30) moving[...,:8] = 0 moving[...,-1:-9:-1] = 0 static[...,:8] = 0 static[...,-1:-9:-1] = 0 #Create the EM metric smooth=25.0 inner_iter=20 step_length=0.25 q_levels=256 double_gradient=True iter_type='gauss_newton' similarity_metric = metrics.EMMetric( 3, smooth, inner_iter, q_levels, double_gradient, iter_type) #Create the optimizer level_iters = [10, 5] opt_tol = 1e-4 inv_iter = 20 inv_tol = 1e-3 ss_sigma_factor = 0.5 optimizer = imwarp.SymmetricDiffeomorphicRegistration(similarity_metric, level_iters, step_length, ss_sigma_factor, opt_tol, inv_iter, inv_tol) optimizer.verbosity = VerbosityLevels.DEBUG mapping = optimizer.optimize(static, moving, None) m = optimizer.get_map() assert_equal(mapping, m) energy_profile = np.array(optimizer.full_energy_profile) if floating is np.float32: expected_profile = \ np.array([144.03694724, 63.06898905, 51.84577681, 39.75409677, 32.10342869, 44.84663951, 38.48587153, 36.64351228, 37.14853803, 40.07766093, 1686.24351443, 1500.19633766, 1302.04852831, 1148.19549508, 1032.820053]) else: expected_profile = \ np.array([144.03695787, 63.06894122, 51.84575143, 39.75308705, 32.13062096, 44.15214831, 40.71952511, 37.26523679, 37.86654915, 34.92844873, 1644.56890565, 1408.15872151, 1274.1339093, 1131.38037004, 1004.71854514]) assert_array_almost_equal(energy_profile, expected_profile, decimal=6)
def test_em_3d_demons(): r""" Test 3D SyN with EM metric, demons-like optimizer Register a volume created by stacking copies of a coronal slice from a T1w brain MRI before and after warping it under a synthetic invertible map. We verify that the final registration is of good quality. """ fname = get_fnames('t1_coronal_slice') nslices = 21 b = 0.1 m = 4 image = np.load(fname) moving, static = get_warped_stacked_image(image, nslices, b, m) # Create the EM metric smooth = 2.0 inner_iter = 20 step_length = 0.25 q_levels = 256 double_gradient = True iter_type = 'demons' similarity_metric = metrics.EMMetric( 3, smooth, inner_iter, q_levels, double_gradient, iter_type) # Create the optimizer level_iters = [20, 5] opt_tol = 1e-4 inv_iter = 20 inv_tol = 1e-3 ss_sigma_factor = 1.0 optimizer = imwarp.SymmetricDiffeomorphicRegistration( similarity_metric, level_iters, step_length, ss_sigma_factor, opt_tol, inv_iter, inv_tol) optimizer.verbosity = VerbosityLevels.DEBUG mapping = optimizer.optimize(static, moving, None) m = optimizer.get_map() assert_equal(mapping, m) warped = mapping.transform(moving) starting_energy = np.sum((static - moving)**2) final_energy = np.sum((static - warped)**2) reduced = 1.0 - final_energy / starting_energy assert(reduced > 0.9)
def test_em_2d_demons(): r''' Register a circle to itself after warping it under a synthetic invertible map. This test is intended to detect regressions only: we saved the energy profile (the sequence of energy values at each iteration) of a working version of EM in 2D, and this test checks that the current energy profile matches the saved one. ''' moving, static = get_synthetic_warped_circle(1) #Configure the metric smooth=25.0 inner_iter=20 q_levels=256 double_gradient=False iter_type='demons' metric = metrics.EMMetric( 2, smooth, inner_iter, q_levels, double_gradient, iter_type) #Configure and run the Optimizer level_iters = [40, 20, 10] optimizer = imwarp.SymmetricDiffeomorphicRegistration(metric, level_iters) optimizer.verbosity = VerbosityLevels.DEBUG mapping = optimizer.optimize(static, moving, None) m = optimizer.get_map() assert_equal(mapping, m) energy_profile = subsample_profile( optimizer.full_energy_profile, 10) print(energy_profile) if USING_VC_SSE2: expected_profile = \ [2.50773393, 3.26942324, 1.81684393, 5.44878881, 40.0195918, 31.87030788, 25.15710409, 29.82206485, 196.33114499, 213.86419995] elif USING_GCC_SSE2: expected_profile = \ [2.50773393, 3.26942352, 1.8168445, 5.44879264, 40.01956373, 31.65616398, 32.43115903, 35.24130742, 192.89072697, 195.456909] assert_array_almost_equal(energy_profile, np.array(expected_profile), decimal=5)
def test_em_2d_gauss_newton(): r''' Register a circle to itself after warping it under a synthetic invertible map. This test is intended to detect regressions only: we saved the energy profile (the sequence of energy values at each iteration) of a working version of EM in 2D, and this test checks that the current energy profile matches the saved one. ''' moving, static = get_synthetic_warped_circle(1) #Configure the metric smooth=25.0 inner_iter=20 q_levels=256 double_gradient=False iter_type='gauss_newton' metric = metrics.EMMetric( 2, smooth, inner_iter, q_levels, double_gradient, iter_type) #Configure and run the Optimizer level_iters = [40, 20, 10] optimizer = imwarp.SymmetricDiffeomorphicRegistration(metric, level_iters) optimizer.verbosity = VerbosityLevels.DEBUG mapping = optimizer.optimize(static, moving, None) m = optimizer.get_map() assert_equal(mapping, m) energy_profile = subsample_profile( optimizer.full_energy_profile, 10) print(energy_profile) if USING_VC_SSE2: expected_profile = \ [2.50773392, 0.41762978, 0.30900322, 0.14818498, 0.44620725, 1.53134054, 1.42115728, 1.66358267, 1.184265, 46.13635772] elif USING_GCC_SSE2: expected_profile = \ [2.50773392, 0.41763383, 0.30908578, 0.06241115, 0.11573476, 2.48475885, 1.10053769, 0.9270271, 49.37186785, 44.72643467] assert_array_almost_equal(energy_profile, np.array(expected_profile), decimal=5)
def test_em_2d_demons(): r''' Register a circle to itself after warping it under a synthetic invertible map. This test is intended to detect regressions only: we saved the energy profile (the sequence of energy values at each iteration) of a working version of EM in 2D, and this test checks that the current energy profile matches the saved one. ''' moving, static = get_synthetic_warped_circle(1) #Configure the metric smooth=25.0 inner_iter=20 q_levels=256 double_gradient=False iter_type='demons' metric = metrics.EMMetric( 2, smooth, inner_iter, q_levels, double_gradient, iter_type) #Configure and run the Optimizer level_iters = [40, 20, 10] optimizer = imwarp.SymmetricDiffeomorphicRegistration(metric, level_iters) optimizer.verbosity = VerbosityLevels.DEBUG mapping = optimizer.optimize(static, moving, None) m = optimizer.get_map() assert_equal(mapping, m) energy_profile = np.array(optimizer.full_energy_profile)[::2] if floating is np.float32: expected_profile = \ [2.50773393, 4.59842633, 3.94307794, 3.09777134, 2.57982865, 3.24937725, 0.42507437, 2.59523238, 29.8114103, 34.94621044, 27.49480758, 38.64567224, 28.14442977, 25.34123425, 36.95076494, 192.36444764, 202.90168694, 188.44310016, 199.73662253, 193.81159141] else: expected_profile = \ [2.50773436, 4.59843299, 3.94307817, 3.09777401, 2.57983375, 3.24936765, 0.42506361, 2.5952175, 29.81143768, 33.42148555, 29.04341476, 29.44541313, 27.39435491, 27.62029669, 187.34889413, 206.57998934, 198.48724278, 188.65410869, 177.83943006] assert_array_almost_equal(energy_profile, np.array(expected_profile))
def test_em_2d_gauss_newton(): r''' Register a circle to itself after warping it under a synthetic invertible map. This test is intended to detect regressions only: we saved the energy profile (the sequence of energy values at each iteration) of a working version of EM in 2D, and this test checks that the current energy profile matches the saved one. ''' moving, static = get_synthetic_warped_circle(1) #Configure the metric smooth=25.0 inner_iter=20 q_levels=256 double_gradient=False iter_type='gauss_newton' metric = metrics.EMMetric( 2, smooth, inner_iter, q_levels, double_gradient, iter_type) #Configure and run the Optimizer level_iters = [40, 20, 10] optimizer = imwarp.SymmetricDiffeomorphicRegistration(metric, level_iters) optimizer.verbosity = VerbosityLevels.DEBUG mapping = optimizer.optimize(static, moving, None) m = optimizer.get_map() assert_equal(mapping, m) energy_profile = np.array(optimizer.full_energy_profile)[::4] if floating is np.float32: expected_profile = \ [2.50773392e+00, 1.19082175e+00, 3.44192871e-01, 4.26320783e-01, 3.77910892e-02, 3.34404847e-01, 3.00400618e+00, 2.56292691e+00, 2.10458398e+00, 2.45479897e+00, 6.14513257e+01, 5.38091115e+01, 5.27868250e+01] else: expected_profile = \ [2.50773436, 1.19082577, 0.34422934, 0.19543193, 0.23659461, 0.41145348, 3.56414698, 3.02325691, 1.74649377, 1.8172007, 2.09930208, 53.06513917, 49.4088898 ] assert_array_almost_equal(energy_profile, np.array(expected_profile))
def test_em_2d_gauss_newton(): r""" Test 2D SyN with EM metric, Gauss-Newton optimizer Register a coronal slice from a T1w brain MRI before and after warping it under a synthetic invertible map. We verify that the final registration is of good quality. """ fname = get_fnames('t1_coronal_slice') nslices = 1 b = 0.1 m = 4 image = np.load(fname) moving, static = get_warped_stacked_image(image, nslices, b, m) # Configure the metric smooth = 5.0 inner_iter = 20 q_levels = 256 double_gradient = False iter_type = 'gauss_newton' metric = metrics.EMMetric(2, smooth, inner_iter, q_levels, double_gradient, iter_type) # Configure and run the Optimizer level_iters = [40, 20, 10] optimizer = imwarp.SymmetricDiffeomorphicRegistration(metric, level_iters) optimizer.verbosity = VerbosityLevels.DEBUG mapping = optimizer.optimize(static, moving, None) m = optimizer.get_map() assert_equal(mapping, m) warped = mapping.transform(moving) starting_energy = np.sum((static - moving)**2) final_energy = np.sum((static - warped)**2) reduced = 1.0 - final_energy / starting_energy assert (reduced > 0.9)