jcreinhold

jcreinhold

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
PyTorch implementation of the Tiramisu network architecture [1]
(2D) Implementation based on [2].

Changes from [2] include:
  1) removal of bias from conv layers,
  2) change zero padding to replication padding,
  3) use of GELU for default activation,

jcreinhold

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Normalize the intensity of a CT image
Author: Jacob Reinhold
"""

import sys
from argparse import ArgumentParser
from pathlib import Path
from typing import Tuple, Union

jcreinhold

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Normalize the intensity of a CT image
Author: Jacob Reinhold
"""

import sys
from argparse import ArgumentParser
from pathlib import Path
from typing import Tuple, Union

jcreinhold

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Normalize the intensity of a set of images by 
finding a tissue mean in the foreground and 
voxel-wise dividing the image by that value
Author: Jacob Reinhold
"""

import os
import re

jcreinhold

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Normalize the intensity of a set of images by 
finding a specified peak of each image foreground intensity
and voxel-wise dividing the image by that value
Author: Jacob Reinhold
"""

import os
import re

jcreinhold

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Normalize the intensity of a set of images by 
finding the specified percentile of each image
foreground and voxel-wise dividing the image by that value
Author: Jacob Reinhold
"""

import os
import re

jcreinhold

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Normalize the intensity of a set of images by 
finding the median 99th percentile of all images
and dividing each image by that value
Author: Jacob Reinhold
"""

import os
import re

jcreinhold

jcreinhold