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brats_dataset.py
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brats_dataset.py
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import os
import numpy as np
import torch
from torch.utils.data import Dataset
import numpy
import random
from torchvision.transforms import functional as F
from torchvision.transforms.functional import InterpolationMode
class BraTSDataset(Dataset):
def __init__(self, root_dir, split_dir="train/", flip=False, resize = None, scale= None, crop=None, version = 1, brightness = False, v_flip = False, rotation = False, random_crop = False, segmentation_type = "whole_tumor"):
self.flip = flip
self.scale = scale
self.resize = resize
self.crop = crop
self.root_dir = root_dir
self.rotation = rotation
self.v_flip = v_flip
self.brightness = brightness
self.random_crop = random_crop
self.split_dir = split_dir
self.flair_dir = 'flair'
self.t1_dir = 't1'
self.mask_dir = 'mask'
self.segmentation_type = segmentation_type
self.version = version
if version == 5:
self.flair_files = numpy.load(self.root_dir+split_dir+'flair_seed0_HGG.npy', allow_pickle='TRUE')
self.t1_files = numpy.load(self.root_dir+split_dir+'t1_seed0_HGG.npy', allow_pickle='TRUE')
self.mask_files = numpy.load(self.root_dir+split_dir+'mask_seed0_HGG.npy', allow_pickle='TRUE')
elif version == 6:
self.flair_files = numpy.load(self.root_dir+split_dir+'flair_seed0_LGG.npy', allow_pickle='TRUE')
self.t1_files = numpy.load(self.root_dir+split_dir+'t1_seed0_LGG.npy', allow_pickle='TRUE')
self.mask_files = numpy.load(self.root_dir+split_dir+'mask_seed0_LGG.npy', allow_pickle='TRUE')
elif version == 7:
self.flair_files = numpy.load(self.root_dir+split_dir+'flair_seed0_HGG.npy', allow_pickle='TRUE')
self.t1_files = numpy.load(self.root_dir+split_dir+'t1_seed0_HGG.npy', allow_pickle='TRUE')
self.mask_files = numpy.load(self.root_dir+split_dir+'mask_seed0_HGG.npy', allow_pickle='TRUE')
### concat HGG and LGG
self.flair_files = np.concatenate((self.flair_files,numpy.load(self.root_dir+split_dir+'flair_seed0_LGG.npy', allow_pickle='TRUE')))
self.t1_files = np.concatenate((self.t1_files,numpy.load(self.root_dir+split_dir+'t1_seed0_LGG.npy', allow_pickle='TRUE')))
self.mask_files = np.concatenate((self.mask_files,numpy.load(self.root_dir+split_dir+'mask_seed0_LGG.npy', allow_pickle='TRUE')))
def __len__(self):
return len(self.flair_files)
def slice_normalize(self,slice):
'''
input: unnormalized slice
OUTPUT: normalized clipped slice
'''
# make sure that percentile below 1% and above 99% are cutoff
b = np.percentile(slice, 99)
t = np.percentile(slice, 1)
slice = np.clip(slice, t, b)
return slice
def __getitem__(self, idx):
flair_image = self.slice_normalize(self.flair_files[idx])
t1_image = self.slice_normalize(self.t1_files[idx])
flair_image = (torch.tensor(flair_image, dtype = torch.float32)).unsqueeze(0)
t1_image = (torch.tensor(t1_image, dtype = torch.float32)).unsqueeze(0)
mask = (torch.tensor(self.mask_files[idx])).unsqueeze(0)
if self.segmentation_type == "whole_tumor":
### make sure that we have only one class
# replace 3 with 1
mask[mask==4] = 1
mask[mask==2] = 1
if self.segmentation_type == "core_tumor":
### make sure that we have only one class
mask[mask==4] = 1
mask[mask==2] = 0
if self.segmentation_type == "ET":
### make sure that we have only one class
mask[mask==1] = 0
mask[mask==2] = 0
mask[mask==4] = 1
flair_image, mask, t1_image = preprocess(flair_image, mask, t1_image, flip= self.flip, resize= self.resize, crop= self.crop, rotation = self.rotation, v_flip =self.v_flip, brightness = self.brightness, random_crop= self.random_crop)
flair_image = (flair_image - flair_image.min())/(flair_image.max() - flair_image.min())
t1_image = (t1_image - t1_image.min())/(t1_image.max()-t1_image.min())
if self.version == 5:
# normalize the image to 0 mean and 1 std using HGG brats dataset statistics
flair_image = (flair_image - 0.14407136)/0.26404217
t1_image = (t1_image - 0.18798836)/0.3249902
return t1_image,mask.squeeze(0), flair_image
class BraTSDataset_3channel_input(Dataset):
def __init__(self, root_dir, split_dir="train/", flip=False, resize = None, scale= None, crop=None, version = 1, brightness = False, v_flip = False, rotation = False, random_crop = False, segmentation_type = "whole_tumor"):
self.flip = flip
self.scale = scale
self.resize = resize
self.crop = crop
self.root_dir = root_dir
self.rotation = rotation
self.v_flip = v_flip
self.brightness = brightness
self.random_crop = random_crop
self.split_dir = split_dir
self.flair_dir = 'flair'
self.t1_dir = 't1'
self.mask_dir = 'mask'
self.segmentation_type = segmentation_type
self.version = version
if version == 5:
self.flair_files = numpy.load(self.root_dir+split_dir+'flair_seed0_HGG.npy', allow_pickle='TRUE')
self.t1_files = numpy.load(self.root_dir+split_dir+'t1_seed0_HGG.npy', allow_pickle='TRUE')
self.mask_files = numpy.load(self.root_dir+split_dir+'mask_seed0_HGG.npy', allow_pickle='TRUE')
elif version == 6:
self.flair_files = numpy.load(self.root_dir+split_dir+'flair_seed0_LGG.npy', allow_pickle='TRUE')
self.t1_files = numpy.load(self.root_dir+split_dir+'t1_seed0_LGG.npy', allow_pickle='TRUE')
self.mask_files = numpy.load(self.root_dir+split_dir+'mask_seed0_LGG.npy', allow_pickle='TRUE')
elif version == 7:
self.flair_files = numpy.load(self.root_dir+split_dir+'flair_seed0_HGG.npy', allow_pickle='TRUE')
self.t1_files = numpy.load(self.root_dir+split_dir+'t1_seed0_HGG.npy', allow_pickle='TRUE')
self.mask_files = numpy.load(self.root_dir+split_dir+'mask_seed0_HGG.npy', allow_pickle='TRUE')
### concat HGG and LGG
self.flair_files = np.concatenate((self.flair_files,numpy.load(self.root_dir+split_dir+'flair_seed0_LGG.npy', allow_pickle='TRUE')))
self.t1_files = np.concatenate((self.t1_files,numpy.load(self.root_dir+split_dir+'t1_seed0_LGG.npy', allow_pickle='TRUE')))
self.mask_files = np.concatenate((self.mask_files,numpy.load(self.root_dir+split_dir+'mask_seed0_LGG.npy', allow_pickle='TRUE')))
def __len__(self):
return len(self.flair_files)
def slice_normalize(self,slice):
'''
input: unnormalized slice
OUTPUT: normalized clipped slice
'''
# make sure that percentile below 1% and above 99% are cutoff
b = np.percentile(slice, 99)
t = np.percentile(slice, 1)
slice = np.clip(slice, t, b)
return slice
def __getitem__(self, idx):
flair_image = self.slice_normalize(self.flair_files[idx])
t1_image = self.slice_normalize(self.t1_files[idx])
flair_image = (torch.tensor(flair_image, dtype = torch.float32)).unsqueeze(0)
t1_image = (torch.tensor(t1_image, dtype = torch.float32)).unsqueeze(0)
mask = (torch.tensor(self.mask_files[idx])).unsqueeze(0)
if self.segmentation_type == "whole_tumor":
### make sure that we have only one class
# replace 3 with 1
mask[mask==4] = 1
mask[mask==2] = 1
if self.segmentation_type == "core_tumor":
### make sure that we have only one class
mask[mask==4] = 1
mask[mask==2] = 0
if self.segmentation_type == "ET":
### make sure that we have only one class
mask[mask==1] = 0
mask[mask==2] = 0
mask[mask==4] = 1
flair_image, mask, t1_image = preprocess(flair_image, mask, t1_image, flip= self.flip, resize= self.resize, crop= self.crop, rotation = self.rotation, v_flip =self.v_flip, brightness = self.brightness, random_crop= self.random_crop)
flair_image = (flair_image - flair_image.min())/(flair_image.max() - flair_image.min())
t1_image = (t1_image - t1_image.min())/(t1_image.max()-t1_image.min())
if self.version == 5:
# normalize the image to 0 mean and 1 std using HGG brats dataset statistics
flair_image = (flair_image - 0.14407136)/0.26404217
t1_image = (t1_image - 0.18798836)/0.3249902
# broast cast all images to 3 channel
flair_image = flair_image.repeat(3,1,1)
t1_image = t1_image.repeat(3,1,1)
return t1_image,mask.squeeze(0), flair_image
def preprocess(flair, mask, t1, flip=False, resize = None, crop=None, rotation = False, v_flip = False, brightness = False, random_crop = False):
if crop:
flair = F.center_crop(flair,crop)
t1 = F.center_crop(t1,crop)
mask = F.center_crop(mask,crop)
if resize:
previous = len(np.unique(mask))
flair = F.resize(flair, size= resize, interpolation =InterpolationMode.BILINEAR, antialias = True)
t1 = F.resize(t1, size= resize, interpolation =InterpolationMode.BILINEAR, antialias = True)
mask = F.resize(mask, size= resize, interpolation =InterpolationMode.NEAREST, antialias = False)
# if len(np.unique(mask)) != previous:
# print ("The classes has been changed due to downsampling")
if random_crop:
_,h,w = mask.shape
if random.random() < 0.5:
new_size = random.uniform(h-40,h-10)
flair = F.center_crop(flair,new_size)
t1 = F.center_crop(t1,new_size)
mask = F.center_crop(mask,new_size)
# make sure to resize back to the original size
flair = F.resize(flair, size= (h,w), interpolation =InterpolationMode.BILINEAR, antialias = True)
t1 = F.resize(t1, size= (h,w), interpolation =InterpolationMode.BILINEAR, antialias = True)
mask = F.resize(mask, size= (h,w), interpolation =InterpolationMode.NEAREST, antialias = False)
if flip:
if random.random() < 0.5:
flair = F.hflip(flair)
t1 = F.hflip(t1)
mask =F.hflip(mask)
if v_flip:
if random.random() < 0.5:
flair = F.vflip(flair)
t1 = F.vflip(t1)
mask =F.vflip(mask)
if rotation:
if random.random() < 0.5:
degree = random.uniform(-20,20)
flair = F.rotate(flair, degree, interpolation = InterpolationMode.BILINEAR)
t1 = F.rotate(t1, degree, interpolation = InterpolationMode.BILINEAR)
mask = F.rotate(mask, degree, interpolation = InterpolationMode.NEAREST)
# use these additional augmentations if necessary
'''
if brightness:
brightness_factor = random.uniform(0.9,1.1)
flair = F.adjust_brightness(flair, brightness_factor)
t1 = F.adjust_brightness(t1, brightness_factor)
if shear:
if random.random() < 0.5:
shear_degree = random.uniform(-10,10)
flair = F.affine(flair, angle =0, translate = (0,0), interpolation = InterpolationMode.BILINEAR)
t1 = F.affine(t1, degree, interpolation = InterpolationMode.BILINEAR)
mask = F.affine(mask, degree, interpolation = InterpolationMode.NEAREST)
'''
mask = mask.long()
return flair, mask, t1
class BraTSDataset_classification(Dataset):
def __init__(self, root_dir, split_dir = "train", version = 5, flip=False, resize = None, scale= None, crop=None, v_flip = False, rotation = False, random_crop = False):
self.flip = flip
self.scale = scale
self.resize = resize
self.crop = crop
self.root_dir = root_dir
self.rotation = rotation
self.v_flip = v_flip
self.random_crop = random_crop
self.split_dir = split_dir
self.version = version
if version == 5:
self.flair_files = numpy.load(self.root_dir+split_dir+'flair_seed0_HGG.npy', allow_pickle='TRUE')
self.t1_files = numpy.load(self.root_dir+split_dir+'t1_seed0_HGG.npy', allow_pickle='TRUE')
self.class_files = numpy.load(self.root_dir+split_dir+'multilabel_classes_seed0_HGG.npy', allow_pickle='TRUE')
elif version == 6:
self.flair_files = numpy.load(self.root_dir+split_dir+'flair_seed0_LGG.npy', allow_pickle='TRUE')
self.t1_files = numpy.load(self.root_dir+split_dir+'t1_seed0_LGG.npy', allow_pickle='TRUE')
self.class_files = numpy.load(self.root_dir+split_dir+'multilabel_classes_seed0_LGG.npy', allow_pickle='TRUE')
def slice_normalize(self,slice):
'''
input: unnormalized slice
OUTPUT: normalized clipped slice
'''
# make sure that percentile below 1% and above 99% are cutoff
b = np.percentile(slice, 99)
t = np.percentile(slice, 1)
slice = np.clip(slice, t, b)
return slice
def __len__(self):
return len(self.flair_files)
def __getitem__(self, idx):
flair_image = self.slice_normalize(self.flair_files[idx])
t1_image = self.slice_normalize(self.t1_files[idx])
flair_image = (torch.tensor(flair_image, dtype = torch.float32)).unsqueeze(0)
t1_image = (torch.tensor(t1_image, dtype = torch.float32)).unsqueeze(0)
classes = (torch.tensor(self.class_files[idx]))
flair_image, t1_image = preprocess_classification(flair_image, t1_image, flip= self.flip, resize= self.resize, crop= self.crop,rotation = self.rotation, v_flip =self.v_flip, random_crop= self.random_crop)
flair_image = (flair_image - flair_image.min())/(flair_image.max() - flair_image.min())
t1_image = (t1_image - t1_image.min())/(t1_image.max()-t1_image.min())
# broast cast all images to 3 channel
flair_image = flair_image.repeat(3,1,1)
t1_image = t1_image.repeat(3,1,1)
# ### normalize the image using the ImageNet Statistics
MEAN = torch.tensor([0.485, 0.456, 0.406])
STD = torch.tensor([0.229, 0.224, 0.225])
flair_image = (flair_image- MEAN[:, None, None]) / STD[:, None, None]
t1_image = (t1_image - MEAN[:, None, None]) / STD[:, None, None]
return flair_image, classes, t1_image
def preprocess_classification(flair, t1, flip=False, resize = None, crop=None,rotation = False, v_flip = False, random_crop = False):
if crop:
flair = F.center_crop(flair,crop)
t1 = F.center_crop(t1,crop)
if resize:
flair = F.resize(flair, size= resize, interpolation =InterpolationMode.BILINEAR, antialias = True)
t1 = F.resize(t1, size= resize, interpolation =InterpolationMode.BILINEAR, antialias = True)
if random_crop:
_,h,w = flair.shape
if random.random() < 0.5:
new_size = random.uniform(h-40,h-10)
flair = F.center_crop(flair,new_size)
t1 = F.center_crop(t1,new_size)
# make sure to resize back to the original size
flair = F.resize(flair, size= (h,w), interpolation =InterpolationMode.BILINEAR, antialias = True)
t1 = F.resize(t1, size= (h,w), interpolation =InterpolationMode.BILINEAR, antialias = True)
if flip:
if random.random() < 0.5:
flair = F.hflip(flair)
t1 = F.hflip(t1)
if v_flip:
if random.random() < 0.5:
flair = F.vflip(flair)
t1 = F.vflip(t1)
if rotation:
if random.random() < 0.5:
degree = random.uniform(-20,20)
flair = F.rotate(flair, degree, interpolation = InterpolationMode.BILINEAR)
t1 = F.rotate(t1, degree, interpolation = InterpolationMode.BILINEAR)
return flair, t1