Updating latest changes DDMR

DeepDeformationMapRegistration/layers/augmentation.py

@@ -133,7 +133,7 @@ class AugmentationLayer(kl.Layer):
             mov_img = tf.zeros_like(fix_img)
             mov_segm = tf.zeros_like(fix_segm)
 
-            disp_map = tf.tile(tf.zeros_like(fix_img), [1, 1, 1, 1, 3])
+            disp_map = tf.tile(tf.zeros_like(fix_img), [1, 1, 1, 1, 3])  # TODO: change, don't use tile!!
 
             if self.out_img_shape is not None:
                 fix_img = self.downsize_image(fix_img)

DeepDeformationMapRegistration/layers/upsampling.py

@@ -485,6 +485,8 @@ def UpInterpolate3D(x,
         nb, nr, nc, nd, nh = tf.TensorShape(x).as_list()
     elif data_format == 'channels_first':
         nb, nh, nr, nc, nd = tf.TensorShape(x).as_list()
+    else:
+        raise ValueError('Invalid option: ', data_format)
 
     r = size[0]
     c = size[1]

DeepDeformationMapRegistration/utils/constants.py

@@ -413,6 +413,7 @@ WAR = 30  # Warning
 ERR = 40  # Error
 DEB = 10  # Debug
 CRI = 50  # Critical
+SUMMARY_LINE_LENGTH = 150
 
 SEVERITY_STR = {INF: 'INFO',
                 WAR: 'WARNING',
@@ -511,8 +512,8 @@ REG_MANUAL_W = [1.] * len(REG_PRIOR_W)
 IXI_DATASET_iso_to_cubic_scales = np.asarray([0.655491 + 0.039223, 0.496783 + 0.029349, 0.499691 + 0.028155])
 # ...OSLO_COMET_CT/Formatted_128x128x128/zoom_factors.csv contain the scale factors of all the training samples from isotropic to 128x128x128
 COMET_DATASET_iso_to_cubic_scales = np.asarray([0.455259 + 0.048027, 0.492012 + 0.044298, 0.577552 + 0.051708])
-MAX_AUG_DISP_ISOT = 30
-MAX_AUG_DEF_ISOT = 6
+MAX_AUG_DISP_ISOT = 30  # mm
+MAX_AUG_DEF_ISOT = 6    # mm
 MAX_AUG_DISP = np.max(MAX_AUG_DISP_ISOT * IXI_DATASET_iso_to_cubic_scales)  # Scaled displacements
 MAX_AUG_DEF = np.max(MAX_AUG_DEF_ISOT * IXI_DATASET_iso_to_cubic_scales)  # Scaled deformations
 MAX_AUG_ANGLE = np.max([np.arctan(np.tan(10*np.pi/180) * IXI_DATASET_iso_to_cubic_scales[1] / IXI_DATASET_iso_to_cubic_scales[0]) * 180 / np.pi,

DeepDeformationMapRegistration/utils/misc.py

@@ -8,6 +8,7 @@ from DeepDeformationMapRegistration.layers.b_splines import interpolate_spline
 from DeepDeformationMapRegistration.utils.thin_plate_splines import ThinPlateSplines
 from tensorflow import squeeze
 from scipy.ndimage import zoom
+import tensorflow as tf
 
 
 def try_mkdir(dir, verbose=True):
@@ -119,15 +120,22 @@ class DisplacementMapInterpolator:
         return disp
 
 
-def get_segmentations_centroids(segmentations, ohe=True, expected_lbls=range(0, 28), missing_centroid=[np.nan]*3, brain_study=True):
+def get_segmentations_centroids(segmentations, ohe=True, expected_lbls=range(1, 28), missing_centroid=[np.nan]*3, brain_study=True):
     segmentations = np.squeeze(segmentations)
     if ohe:
-        segmentations = np.sum(segmentations, axis=-1).astype(np.uint8)
-        missing_lbls = set(expected_lbls) - set(np.unique(segmentations))
-        if brain_study:
-            segmentations += np.ones_like(segmentations)  # Regionsprops neglect the label 0. But we need it, so offset all labels by 1
+        segmentations = segmentation_ohe_to_cardinal(segmentations)
+        lbls = set(np.unique(segmentations)) - {0}  # Remove the 0 value returned by np.unique, no label
+        # missing_lbls = set(expected_lbls) - lbls
+        # if brain_study:
+        #     segmentations += np.ones_like(segmentations)  # Regionsprops neglect the label 0. But we need it, so offset all labels by 1
     else:
-        missing_lbls = set(expected_lbls) - set(np.unique(segmentations))
+        lbls = set(np.unique(segmentations)) if 0 in expected_lbls else set(np.unique(segmentations)) - {0}
+    missing_lbls = set(expected_lbls) - lbls
+
+    if 0 in expected_lbls:
+        segmentations += np.ones_like(segmentations)  # Regionsprops neglects the label 0. But we need it, so offset all labels by 1
+
+    segmentations = np.squeeze(segmentations)   # remove channel dimension, not needed anyway
 
     seg_props = regionprops(segmentations)
     centroids = np.asarray([c.centroid for c in seg_props]).astype(np.float32)
@@ -147,11 +155,15 @@ def segmentation_ohe_to_cardinal(segmentation):
     return np.argmax(cpy, axis=-1)[..., np.newaxis]
 
 
-def segmentation_cardinal_to_ohe(segmentation):
+def segmentation_cardinal_to_ohe(segmentation, labels_list: list = None):
     # Keep in mind that we don't handle the overlap between the segmentations!
-    cpy = np.tile(np.zeros_like(segmentation), (1, 1, 1, len(np.unique(segmentation)[1:])))
-    for ch, lbl in enumerate(np.unique(segmentation)[1:]):
-        cpy[segmentation == lbl, ch] = 1
+    #labels_list = np.unique(segmentation)[1:] if labels_list is None else labels_list
+    num_labels = len(labels_list)
+    expected_shape = segmentation.shape[:-1] + (num_labels,)
+    cpy = np.zeros(expected_shape, dtype=np.uint8)
+    seg_squeezed = np.squeeze(segmentation, axis=-1)
+    for ch, lbl in enumerate(labels_list):
+        cpy[seg_squeezed == lbl, ch] = 1
     return cpy
 
 
@@ -184,3 +196,50 @@ def scale_transformation(original_shape: [list, tuple, np.ndarray], dest_shape:
 
     return trf
 
+
+class GaussianFilter:
+    def __init__(self, size, sigma, dim, num_channels, stride=None, batch: bool=True):
+        """
+        Gaussian filter
+        :param size: Kernel size
+        :param sigma: Sigma of the Gaussian filter.
+        :param dim: Data dimensionality. Must be {2, 3}.
+        :param num_channels: Number of channels of the image to filter.
+        """
+        self.size = size
+        self.dim = dim
+        self.sigma = float(sigma)
+        self.num_channels = num_channels
+        self.stride = size // 2 if stride is None else int(stride)
+        if batch:
+            self.stride = [1] + [self.stride] * self.dim + [1]   # No support for strides in the batch and channel dims
+        else:
+            self.stride = [self.stride] * self.dim + [1]    # No support for strides in the batch and channel dims
+
+        self.convDN = getattr(tf.nn, 'conv%dd' % dim)
+        self.__GF = None
+
+        self.__build_gaussian_filter()
+
+    def __build_gaussian_filter(self):
+        range_1d = tf.range(-(self.size/2) + 1, self.size//2 + 1)
+        g_1d = tf.math.exp(-1.0 * tf.pow(range_1d, 2) / (2. * tf.pow(self.sigma, 2)))
+        g_1d_expanded = tf.expand_dims(g_1d, -1)
+        iterator = tf.constant(1)
+        self.__GF = tf.while_loop(lambda iterator, g_1d: tf.less(iterator, self.dim),
+                                  lambda iterator, g_1d: (iterator + 1, tf.expand_dims(g_1d, -1) * tf.transpose(g_1d_expanded)),
+                                  [iterator, g_1d],
+                                  [iterator.get_shape(), tf.TensorShape(None)],  # Shape invariants
+                                  back_prop=False
+                                  )[-1]
+
+        self.__GF = tf.divide(self.__GF, tf.reduce_sum(self.__GF))  # Normalization
+        self.__GF = tf.reshape(self.__GF, (*[self.size]*self.dim, 1, 1))  # Add Ch_in and Ch_out for convolution
+        self.__GF = tf.tile(self.__GF, (*[1] * self.dim, self.num_channels, self.num_channels,))
+
+    def apply_filter(self, in_image):
+        return self.convDN(in_image, self.__GF, self.stride, 'SAME')
+
+    @property
+    def kernel(self):
+        return self.__GF

DeepDeformationMapRegistration/utils/operators.py

@@ -63,3 +63,18 @@ def sample_unique(population, samples, tout=tf.int32):
     _, indices = tf.nn.top_k(z, samples)
     ret_val = tf.gather(population, indices)
     return tf.cast(ret_val, tout)
+
+
+def safe_medpy_metric(prediction, reference, nb_labels, metric_fnc, fnc_args: dict={}):
+    vals = list()
+    if 'voxelspacing' in fnc_args.keys():
+        diag = np.power(reference.shape[:-1] * fnc_args['voxelspacing'], 2)
+    else:
+        diag = np.power(reference.shape[:-1], 2)
+    diag = np.sqrt(np.sum(diag))
+    for l in range(nb_labels):
+        try:
+            vals.append(metric_fnc(prediction[..., l], reference[..., l], **fnc_args))
+        except RuntimeError:
+            vals.append(diag)
+    return vals

DeepDeformationMapRegistration/utils/visualization.py

@@ -1,5 +1,5 @@
 import matplotlib
-#matplotlib.use('TkAgg')
+matplotlib.use('WebAgg')
 import matplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D
 import matplotlib.colors as mcolors
@@ -17,7 +17,7 @@ THRES = 0.9
 
 # COLOR MAPS
 chunks = np.linspace(0, 1, 10)
-cmap1 = plt.get_cmap('hsv', 4)
+cmap1 = plt.get_cmap('hsv', 30)
 # cmaplist = [cmap1(i) for i in range(cmap1.N)]
 cmaplist = [(1, 1, 1, 1), (0, 0, 1, 1), (230 / 255, 97 / 255, 1 / 255, 1), (128 / 255, 0 / 255, 32 / 255, 1)]
 cmaplist[0] = (1, 1, 1, 1.0)
@@ -34,6 +34,14 @@ cmap4 = mcolors.LinearSegmentedColormap.from_list('mycmap', colors, N=100)
 
 cmap_bin = cm.get_cmap('viridis', 3)  # viridis is the default colormap
 
+cmap_segs = np.asarray([mcolors.to_rgba(mcolors.CSS4_COLORS[c], 1) for c in mcolors.CSS4_COLORS.keys()])
+cmap_segs.sort()
+# rnd_idxs = [30, 17, 72, 90, 74, 39, 120, 63, 52,  79, 140, 68, 131, 109, 57, 49, 11, 132, 29, 46, 51, 26, 53,  7, 89, 47, 43, 121, 31, 28, 106, 92, 130, 117, 91, 118, 61,  5, 80, 93, 58, 133, 14, 98, 116, 76, 113, 111, 136, 142, 95, 122, 86, 77, 36, 97, 141, 115, 18, 81, 88, 87, 44, 146, 103, 67, 147, 48, 42, 83, 128, 65, 139, 69, 27, 135, 94, 134, 50, 19, 114,  0, 96, 10, 138, 75, 13, 12, 102, 32, 66, 16,  8, 73, 85, 145, 54, 37, 70, 143]
+# cmap_segs = cmap_segs[rnd_idxs]
+np.random.shuffle(cmap_segs)
+cmap_segs[0, -1] = 0
+cmap_segs = mcolors.ListedColormap(cmap_segs)
+
 
 def view_centerline_sample(sample: np.ndarray, dimensionality: int, ax=None, c=None, name=None):
     if dimensionality == 2:
@@ -321,7 +329,7 @@ def save_centreline_img(img, title, filename, fig=None):
     plt.close()
 
 
-def save_disp_map_img(disp_map, title, filename, affine_transf=False, fig=None, show=False):
+def save_disp_map_img(disp_map, title, filename, affine_transf=False, fig=None, show=False, step=1):
     if fig is not None:
         fig.clear()
         plt.figure(fig.number)
@@ -333,7 +341,7 @@ def save_disp_map_img(disp_map, title, filename, affine_transf=False, fig=None,
     if dim == 2:
         ax_x = fig.add_subplot(131)
         ax_x.set_title('H displacement')
-        im_x = ax_x.imshow(disp_map[..., C.H_DISP])
+        im_x = ax_x.imshow(disp_map[..., ::step, ::step, C.H_DISP])
         ax_x.tick_params(axis='both',
                        which='both',
                        bottom=False,
@@ -344,7 +352,7 @@ def save_disp_map_img(disp_map, title, filename, affine_transf=False, fig=None,
 
         ax_y = fig.add_subplot(132)
         ax_y.set_title('W displacement')
-        im_y = ax_y.imshow(disp_map[..., C.W_DISP])
+        im_y = ax_y.imshow(disp_map[..., ::step, ::step, C.W_DISP])
         ax_y.tick_params(axis='both',
                          which='both',
                          bottom=False,
@@ -371,7 +379,7 @@ def save_disp_map_img(disp_map, title, filename, affine_transf=False, fig=None,
                     ax.text(i, j, transf_mat[i, j], ha="center", va="center", color="b")
 
         else:
-            c, d, s = _prepare_quiver_map(disp_map, dim=dim)
+            c, d, s = _prepare_quiver_map(disp_map, dim=dim, spc=step)
             im = ax.imshow(s, interpolation='none', aspect='equal')
             ax.quiver(c[C.H_DISP], c[C.W_DISP], d[C.H_DISP], d[C.W_DISP],
                       scale=C.QUIVER_PARAMS.arrow_scale)
@@ -386,7 +394,7 @@ def save_disp_map_img(disp_map, title, filename, affine_transf=False, fig=None,
         fig.suptitle(title)
     else:
         ax = fig.add_subplot(111, projection='3d')
-        c, d, s = _prepare_quiver_map(disp_map[0, ...], dim=dim)
+        c, d, s = _prepare_quiver_map(disp_map[0, ...], dim=dim, spc=step)
         ax.quiver(c[C.H_DISP], c[C.W_DISP], c[C.D_DISP], d[C.H_DISP], d[C.W_DISP], d[C.D_DISP])
         _square_3d_plot(np.arange(0, dim_h-1), np.arange(0, dim_w-1), np.arange(0, dim_d-1), ax)
         fig.suptitle('Displacement map')
@@ -810,7 +818,12 @@ def plot_dataset_3d(img_sets):
     return fig
 
 
-def plot_predictions(fix_img_batch, mov_img_batch, disp_map_batch, pred_img_batch, filename='predictions', fig=None, show=False):
+def plot_predictions(img_batches, disp_map_batch, seg_batches=None, step=1, filename='predictions', fig=None, show=False):
+    fix_img_batch, mov_img_batch, pred_img_batch = img_batches
+    if seg_batches != None:
+        fix_seg_batch, mov_seg_batch, pred_seg_batch = seg_batches
+    else:
+        fix_seg_batch = mov_seg_batch = pred_seg_batch = None
     num_rows = fix_img_batch.shape[0]
     img_dim = len(fix_img_batch.shape) - 2
     img_size = fix_img_batch.shape[1:-1]
@@ -828,6 +841,10 @@ def plot_predictions(fix_img_batch, mov_img_batch, disp_map_batch, pred_img_batc
         fix_img_batch = fix_img_batch[:, selected_slice, ...]
         mov_img_batch = mov_img_batch[:, selected_slice, ...]
         pred_img_batch = pred_img_batch[:, selected_slice, ...]
+        if seg_batches != None:
+            fix_seg_batch = fix_seg_batch[:, selected_slice, ...]
+            mov_seg_batch = mov_seg_batch[:, selected_slice, ...]
+            pred_seg_batch = pred_seg_batch[:, selected_slice, ...]
         disp_map_batch = disp_map_batch[:, selected_slice, ..., 1:]  # Only the sagittal and longitudinal axes
         img_size = fix_img_batch.shape[1:-1]
     elif img_dim != 2:
@@ -836,16 +853,24 @@ def plot_predictions(fix_img_batch, mov_img_batch, disp_map_batch, pred_img_batc
     for row in range(num_rows):
         fix_img = fix_img_batch[row, :, :, 0].transpose()
         mov_img = mov_img_batch[row, :, :, 0].transpose()
-        disp_map = disp_map_batch[row, :, :, :].transpose((1, 0, 2))
         pred_img = pred_img_batch[row, :, :, 0].transpose()
-        ax[row, 0].imshow(fix_img, origin='lower')
+        if seg_batches != None:
+            fix_seg = fix_seg_batch[row, :, :, 0].transpose()
+            mov_seg= mov_seg_batch[row, :, :, 0].transpose()
+            pred_seg = pred_seg_batch[row, :, :, 0].transpose()
+        disp_map = disp_map_batch[row, :, :, :].transpose((1, 0, 2))
+        ax[row, 0].imshow(fix_img, origin='lower', cmap='gray')
+        if seg_batches != None:
+            ax[row, 0].imshow(fix_seg, origin='lower', cmap=cmap_segs)
         ax[row, 0].tick_params(axis='both',
                                which='both',
                                bottom=False,
                                left=False,
                                labelleft=False,
                                labelbottom=False)
-        ax[row, 1].imshow(mov_img, origin='lower')
+        ax[row, 1].imshow(mov_img, origin='lower', cmap='gray')
+        if seg_batches != None:
+            ax[row, 1].imshow(mov_seg, origin='lower', cmap=cmap_segs)
         ax[row, 1].tick_params(axis='both',
                                which='both',
                                bottom=False,
@@ -853,7 +878,7 @@ def plot_predictions(fix_img_batch, mov_img_batch, disp_map_batch, pred_img_batc
                                labelleft=False,
                                labelbottom=False)
 
-        c, d, s = _prepare_quiver_map(disp_map, spc=5)
+        c, d, s = _prepare_quiver_map(disp_map, spc=step)
         cx, cy = c
         dx, dy = d
         disp_map_color = _prepare_colormap(disp_map)
@@ -866,7 +891,9 @@ def plot_predictions(fix_img_batch, mov_img_batch, disp_map_batch, pred_img_batc
                                labelleft=False,
                                labelbottom=False)
 
-        ax[row, 3].imshow(mov_img, origin='lower')
+        ax[row, 3].imshow(mov_img, origin='lower', cmap='gray')
+        if seg_batches != None:
+            ax[row, 3].imshow(mov_seg, origin='lower', cmap=cmap_segs)
         ax[row, 3].quiver(cx, cy, dx, dy, units='dots', scale=1, color='w')
         ax[row, 3].tick_params(axis='both',
                                which='both',
@@ -875,7 +902,9 @@ def plot_predictions(fix_img_batch, mov_img_batch, disp_map_batch, pred_img_batc
                                labelleft=False,
                                labelbottom=False)
 
-        ax[row, 4].imshow(pred_img, origin='lower')
+        ax[row, 4].imshow(pred_img, origin='lower', cmap='gray')
+        if seg_batches != None:
+            ax[row, 4].imshow(pred_seg, origin='lower', cmap=cmap_segs)
         ax[row, 4].tick_params(axis='both',
                                which='both',
                                bottom=False,

requirements.txt

@@ -27,20 +27,23 @@ et-xmlfile==1.0.1
 fastrlock==0.6
 flatbuffers==1.12
 future==0.18.2
-gast==0.4.0
+gast==0.2.2
 google-auth==1.35.0
 google-auth-oauthlib==0.4.6
 google-pasta==0.2.0
 googleapis-common-protos==1.53.0
 grpcio==1.40.0
-h5py==3.1.0
+h5py==2.10.0
 idna==2.10
 imageio==2.9.0
 importlib-metadata==3.4.0
 importlib-resources==5.2.2
+ipydatawidgets==4.2.0
 ipykernel==5.5.3
 ipython==7.16.1
 ipython-genutils==0.2.0
+ipyvolume==0.5.2
+ipywebrtc==0.6.0
 ipywidgets==7.6.3
 jedi==0.18.0
 Jinja2==2.11.3
@@ -84,7 +87,7 @@ patsy==0.5.1
 pexpect==4.8.0
 pickleshare==0.7.5
 Pillow==8.1.0
-pkg-resources==0.0.0
+pkg_resources==0.0.0
 plotly==4.14.3
 plyfile==0.7.3
 probreg==0.3.1
@@ -107,6 +110,7 @@ pyrsistent==0.17.3
 pystrum==0.1
 python-dateutil==2.8.1
 python-utils==2.5.6
+pythreejs==2.3.0
 pytz==2021.1
 PyWavelets==1.1.1
 PyYAML==5.4.1
@@ -123,13 +127,13 @@ SimpleITK==2.0.2
 six==1.15.0
 sklearn==0.0
 statsmodels==0.12.2
-tensorboard==1.13.1
+tabulate==0.8.9
+tensorboard==1.14.0
 tensorboard-data-server==0.6.1
-tensorboard-plugin-wit==1.8.0
 tensorflow-addons==0.14.0
 tensorflow-datasets==4.4.0
-tensorflow-estimator==1.13.0
-tensorflow-gpu==1.13.1
+tensorflow-estimator==1.14.0
+tensorflow-gpu==1.14.0
 tensorflow-metadata==1.2.0
 termcolor==1.1.0
 terminado==0.9.4
@@ -141,6 +145,7 @@ tikzplotlib==0.9.7
 tornado==6.1
 tqdm==4.56.0
 traitlets==4.3.3
+traittypes==0.2.1
 transformations==2020.1.1
 trimesh==3.9.29
 typeguard==2.12.1