def save_segmentation(self, frame, image_size, filename, spacing):
image_size = [image_size[1], image_size[0]]
# Create compounded segmentation object
segmentation = self.get_object_segmentation(image_size, frame)
segmentation_mhd = MetaImage(data=segmentation)
segmentation_mhd.set_attribute('ImageQuality',
frame.image_annotation.image_quality)
segmentation_mhd.set_spacing(spacing)
metadata = ImageMetadata.objects.filter(
image=frame.image_annotation.image)
for item in metadata:
segmentation_mhd.set_attribute(item.name, item.value)
segmentation_mhd.write(filename)
def save_segmentation(self, frame, image_size, filename, spacing,
x_scaling):
print('X scaling is', x_scaling)
image_size = [image_size[1], image_size[0]]
# Get control points for all objects
control_points0 = list(
ControlPoint.objects.filter(image=frame,
object=0).order_by('index'))
control_points1 = list(
ControlPoint.objects.filter(image=frame,
object=1).order_by('index'))
control_points2 = list(
ControlPoint.objects.filter(image=frame,
object=2).order_by('index'))
if x_scaling != 1:
for point in control_points0:
point.x *= x_scaling
for point in control_points1:
point.x *= x_scaling
for point in control_points2:
point.x *= x_scaling
# Endpoints of object 2 are the same as object 1
if len(control_points0) > 0 and len(control_points2) > 0:
control_points2.insert(0, control_points0[-1])
control_points2.append(control_points0[0])
# Create new endpoints for object 1
if len(control_points1) > 0:
point = self.calculate_new_endpoints(control_points0,
control_points1[0])
control_points1.insert(0, point)
point = self.calculate_new_endpoints(control_points0,
control_points1[-1])
control_points1.append(point)
# Create compounded segmentation object
#image_size[1] = int(round(image_size[1]/x_scaling))
#spacing[1] = spacing[0]
segmentation = np.zeros(image_size, dtype=np.uint8)
if len(control_points1) > 0:
object_segmentation = self.get_object_segmentation(
image_size, control_points1, x_scaling)
segmentation[object_segmentation == 1] = 2 # Draw epi before endo
if len(control_points2) > 0:
object_segmentation = self.get_object_segmentation(
image_size, control_points2, x_scaling)
segmentation[object_segmentation == 1] = 3
if len(control_points0) > 0:
object_segmentation = self.get_object_segmentation(
image_size, control_points0, x_scaling)
segmentation[object_segmentation == 1] = 1
segmentation_mhd = MetaImage(data=segmentation)
segmentation_mhd.set_attribute('ImageQuality',
frame.image_annotation.image_quality)
segmentation_mhd.set_attribute('FrameType', frame.frame_metadata)
segmentation_mhd.set_spacing(spacing)
metadata = ImageMetadata.objects.filter(
image=frame.image_annotation.image)
for item in metadata:
segmentation_mhd.set_attribute(item.name, item.value)
segmentation_mhd.write(filename)
def save_segmentation(self, frame, image_size, filename, spacing,
x_scaling, image):
print('X scaling is', x_scaling)
image_size = [image_size[1], image_size[0]]
# Get control points for all objects
control_points0 = list(
ControlPoint.objects.filter(image=frame,
object=0).order_by('index'))
control_points1 = list(
ControlPoint.objects.filter(image=frame,
object=1).order_by('index'))
control_points2 = list(
ControlPoint.objects.filter(image=frame,
object=2).order_by('index'))
control_points3 = list(
ControlPoint.objects.filter(image=frame,
object=3).order_by('index'))
if x_scaling != 1:
for point in control_points0:
point.x *= x_scaling
for point in control_points1:
point.x *= x_scaling
for point in control_points2:
point.x *= x_scaling
for point in control_points3:
point.x *= x_scaling
# Endpoints of object 2 (LA) are the same as object 0 (endo/LV)
if len(control_points0) > 0 and len(control_points2) > 0:
control_points2.insert(0, control_points0[-1])
control_points2.append(control_points0[0])
# Aorta (3) endpoints
if len(control_points3) > 0 and len(control_points0) > 0:
control_points3.insert(0, control_points0[-2])
control_points3.append(control_points0[-1])
# (myocard/epi) (1) endpoints
if len(control_points0) > 0 and len(control_points1) > 0:
control_points1.insert(0, control_points0[0])
control_points1.append(control_points0[-2])
# Create compounded segmentation object
#image_size[1] = int(round(image_size[1]/x_scaling))
#spacing[1] = spacing[0]
segmentation = np.zeros(image_size, dtype=np.uint8)
if len(control_points1) > 0:
object_segmentation = self.get_object_segmentation(
image_size,
control_points1,
x_scaling,
straight_lines=[[0, -1], [-2, -1], [0, 1]])
segmentation[object_segmentation == 1] = 2 # Draw epi before endo
if len(control_points2) > 0:
object_segmentation = self.get_object_segmentation(
image_size,
control_points2,
x_scaling,
straight_lines=[[0, -1]])
segmentation[object_segmentation == 1] = 3
if len(control_points0) > 0:
object_segmentation = self.get_object_segmentation(
image_size,
control_points0,
x_scaling,
straight_lines=[[0, -1], [-2, -1]])
segmentation[object_segmentation == 1] = 1
if len(control_points3) > 0:
object_segmentation = self.get_object_segmentation(
image_size,
control_points3,
x_scaling,
straight_lines=[[0, -1]])
segmentation[object_segmentation == 1] = 4
segmentation_mhd = MetaImage(data=segmentation)
segmentation_mhd.set_attribute('ImageQuality',
frame.image_annotation.image_quality)
segmentation_mhd.set_attribute('FrameType', frame.frame_metadata)
segmentation_mhd.set_spacing(spacing)
metadata = ImageMetadata.objects.filter(
image=frame.image_annotation.image)
for item in metadata:
segmentation_mhd.set_attribute(item.name, item.value)
segmentation_mhd.write(filename)
def save_segmentation(self, frame, image_size, filename, spacing, x_scaling, image):
print('X scaling is', x_scaling)
image_size = [image_size[1], image_size[0]]
# Get control points for all objects
control_points0 = list(ControlPoint.objects.filter(image=frame, object=0).order_by('index'))
control_points1 = list(ControlPoint.objects.filter(image=frame, object=1).order_by('index'))
control_points2 = list(ControlPoint.objects.filter(image=frame, object=2).order_by('index'))
control_points3 = list(ControlPoint.objects.filter(image=frame, object=3).order_by('index'))
control_points4 = list(ControlPoint.objects.filter(image=frame, object=4).order_by('index'))
control_points5 = list(ControlPoint.objects.filter(image=frame, object=5).order_by('index'))
if x_scaling != 1:
for point in control_points0:
point.x *= x_scaling
for point in control_points1:
point.x *= x_scaling
for point in control_points2:
point.x *= x_scaling
for point in control_points3:
point.x *= x_scaling
for point in control_points4:
point.x *= x_scaling
for point in control_points5:
point.x *= x_scaling
# Endpoints of object 2 (LA) are the same as object 0 (endo/LV)
if len(control_points0) > 0 and len(control_points2) > 0:
control_points2.insert(0, control_points0[-1])
control_points2.append(control_points0[0])
# Aorta (3) endpoints
if len(control_points3) > 0 and len(control_points0) > 0:
control_points3.insert(0, control_points0[-2])
control_points3.append(control_points0[-1])
# (LVOT) (5) endpoints
if len(control_points0) > 0 and len(control_points5) > 0:
control_points5.insert(0, control_points0[-1])
control_points5.append(control_points0[-2])
# (myocard/epi) (1) endpoints
if len(control_points0) > 0 and len(control_points1) > 0:
control_points1.insert(0, control_points0[0])
control_points1.append(control_points0[-2])
# Create compounded segmentation object
#image_size[1] = int(round(image_size[1]/x_scaling))
#spacing[1] = spacing[0]
segmentation = np.zeros(image_size, dtype=np.uint8)
if len(control_points1) > 0:
object_segmentation = self.get_object_segmentation(image_size, control_points1, x_scaling, straight_lines=[[0, -1], [-2, -1], [0, 1]])
segmentation[object_segmentation == 1] = 2 # Draw epi before endo
if len(control_points2) > 0:
object_segmentation = self.get_object_segmentation(image_size, control_points2, x_scaling, straight_lines=[[0, -1]])
segmentation[object_segmentation == 1] = 3
if len(control_points0) > 0:
object_segmentation = self.get_object_segmentation(image_size, control_points0, x_scaling, straight_lines=[[0, -1], [-2, -1]])
segmentation[object_segmentation == 1] = 1
if len(control_points3) > 0:
object_segmentation = self.get_object_segmentation(image_size, control_points3, x_scaling, straight_lines=[[0, -1]])
segmentation[object_segmentation == 1] = 4
if len(control_points4) > 0:
object_segmentation = self.get_object_segmentation(image_size, control_points4, x_scaling)
segmentation[object_segmentation == 1] = 5
if len(control_points5) > 0:
object_segmentation = self.get_object_segmentation(image_size, control_points5, x_scaling, straight_lines=[[0, -1], [-2, -1], [-3, -2], [-4, -3]])
segmentation[np.logical_and(object_segmentation == 1, segmentation != 2)] = 6 # Draw LVOT after endo, but epi should be subtracted
# Remove data outside ultrasound sector
segmentation[image == 0] = 0 # TODO improve this maybe?
segmentation_mhd = MetaImage(data=segmentation)
segmentation_mhd.set_attribute('ImageQuality', frame.image_annotation.image_quality)
segmentation_mhd.set_attribute('FrameType', frame.frame_metadata)
segmentation_mhd.set_spacing(spacing)
metadata = ImageMetadata.objects.filter(image=frame.image_annotation.image)
for item in metadata:
segmentation_mhd.set_attribute(item.name, item.value)
segmentation_mhd.write(filename)