def load_channels(self, normalize=False):
modalities = []
modalities.append(nib.load(self.FLAIR_FILE))
modalities.append(nib.load(self.T1_FILE))
modalities.append(nib.load(self.T1c_FILE))
modalities.append(nib.load(self.T2_FILE))
channels = np.zeros(modalities[0].shape + (4, ), dtype=np.float32)
if normalize:
mask = self.load_ROI_mask()
for index_mod, mod in enumerate(modalities):
if self.data_augmentation_flag:
channels[:, :, :,
index_mod] = flip_plane(np.asarray(mod.dataobj),
plane=self.data_augmentation)
else:
channels[:, :, :, index_mod] = np.asarray(mod.dataobj)
if normalize:
channels[:, :, :,
index_mod] = normalize_image(channels[:, :, :,
index_mod],
mask=mask)
return channels
def load_labels(self):
labels_proxy = nib.load(self.GT_FILE)
if self.data_augmentation:
labels = flip_plane(np.asarray(labels_proxy.dataobj))
else:
labels = np.asarray(labels_proxy.dataobj)
labels[np.where(labels == 2)] = 0 # To get rid of other pathologies
return labels
def load_ROI_mask(self):
roimask_proxy = nib.load(self.ROIMASK_FILE)
if self.data_augmentation:
mask = flip_plane(np.asarray(roimask_proxy.dataobj))
else:
mask = np.asarray(roimask_proxy.dataobj)
return mask.astype('bool')
def load_tumor_mask(self):
tumormask_proxy = nib.load(self.TUMORMASK_FILE)
if self.data_augmentation_flag:
mask = flip_plane(np.asarray(tumormask_proxy.dataobj),
plane=self.data_augmentation)
else:
mask = np.asarray(tumormask_proxy.dataobj)
return mask.astype('bool')
def load_labels(self):
labels_proxy = nib.load(self.GT_FILE)
if self.data_augmentation_flag:
labels = flip_plane(np.squeeze(np.asarray(labels_proxy.dataobj)),
plane=self.data_augmentation)
else:
labels = np.squeeze(np.asarray(labels_proxy.dataobj))
return labels
def load_labels(self):
labels_proxy = nib.load(self.GT_FILE)
if self.data_augmentation:
labels = flip_plane(np.asarray(labels_proxy.dataobj),
plane=self.data_augmentation)
else:
labels = np.asarray(labels_proxy.dataobj)
unique_labels = np.unique(labels)
unique_labels = np.sort(unique_labels)
for it_u_l, u_l in enumerate(unique_labels):
labels[np.where(labels == u_l)] = it_u_l
return labels