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brats15_2d.py
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brats15_2d.py
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# coding:utf-8
"""
对 whole tumor 任务
基于原始 150 * 240 * 240 图，抠出非零区域.
据此寻找label 是 1,2,3,4的部分
对 tumor core 任务
从原始图中抠出 label 是1，2，3，4 的区域
寻找label 是 1,3,4的部分
对 enhanced tumor 任务
从原始图中抠出 label 是1，3，4 的区域
寻找label 是 4 的部分
对Flair 数据，范围是 R+; T1 范围是 R; T1C 范围是 R+; T2 范围是 R+;
"""
import os
import torch.nn as nn
import torch
from torch.utils.data import Dataset
import numpy as np
import scipy.misc
from src.utils import *
ddd = ['flair', 't1', 't1c', 't2']
class Brats15DataLoader(Dataset):
def __init__(self, data_dir, is_train, direction='axial', task_type='wt',
conf='../config/train15.conf'):
self.data_dir = data_dir #
self.img_lists = []
self.volume_size = 16
self.data_box = [144, 192, 192] # max 145
self.margin = 0
self.is_train = is_train # True or False
self.task_type = task_type # whole tumor, tumor core,
self.direction = direction # 'axial', 'sagittal', or 'coronal'
train_config = open(conf).readlines()
for data in train_config:
self.img_lists.append(os.path.join(self.data_dir, data.strip('\n')))
print('~' * 50)
print ('******** Loading data from disk ********')
self.data = []
for subject in self.img_lists:
volume, label = self.get_subject(subject) # 4 * 144 * 192 * 192
length = volume.shape[1] # 144
for i in range(length):
name = subject + '=' + str(i)
self.data.append([volume[:, i, :, :], label[:, i, :, :], name])
print ('******** Finish loading data ********')
print ('******** Total number of 2D images is ' + str(len(self.data)))
print('~' * 50)
def __len__(self):
return len(self.data)
def __getitem__(self, index):
# ********** get file dir **********
[image2d, label2d,name] = self.data[index] # get whole data for one subject
# ********** change data type from numpy to torch.Tensor **********
image2d = torch.from_numpy(image2d).float() # Float Tensor 4 * 144 * 192 * 192
label2d = torch.from_numpy(label2d).float() # Float Tensor 4 * 144 * 192 * 192
return image2d, label2d, name
def get_subject(self, subject):
"""
get
:param subject: absolute dir
:return:
volume 4D numpy 4 * 144 * 192 * 192
label 4D numpy 4 * 144 * 192 * 192
"""
# **************** get file ****************
files = os.listdir(subject) # [XXX.Flair, XXX.T1, XXX.T1c, XXX.T2, XXX.OT]
multi_mode_dir = []
label_dir = ""
for f in files:
if f == '.DS_Store':
continue
# if is data
if 'Flair' in f or 'T1' in f or 'T2' in f:
multi_mode_dir.append(f)
elif 'OT.' in f: # if is label
label_dir = f
bbmin = [0, 0, 0] # default bounding box
bbmax = [155 - 1, 240 - 1, 240 - 1]
# ********** load 4 mode images **********
multi_mode_imgs = [] # list size :4 item size: 150 * 240 * 240
for mod_dir in multi_mode_dir:
path = os.path.join(subject, mod_dir) # absolute directory
img = load_mha_as_array(path + '/' + mod_dir + '.mha')
multi_mode_imgs.append(img)
if 'Flair.' in mod_dir: # get non zero bounding box based on Flair image
bbmin, bbmax = get_ND_bounding_box(img, self.margin)
# ********** get label **********
label_dir = os.path.join(subject, label_dir) + '/' + label_dir + '.mha'
label = load_mha_as_array(label_dir) #
# *********** image pre-processing *************
# step1 ****** resize images and labels to 160 * 192 * 192 *******
for i in range(len(multi_mode_imgs)):
multi_mode_imgs[i] = crop_with_box(multi_mode_imgs[i], bbmin, bbmax, self.data_box)
multi_mode_imgs[i] = normalize_one_volume(multi_mode_imgs[i])
label = crop_with_box(label, bbmin, bbmax, self.data_box) # (144, 192, 192)
# step2 ********* transfer to different direction *********
multi_mode_imgs = transpose_volumes(multi_mode_imgs, self.direction) # list
label = transpose_volumes([label], self.direction)[0]
if self.direction == 'sagittal' or self.direction == 'coronal':
self.data_box = [192, 144, 192]
# step3 ********** get bounding box based on task **********
if self.task_type == 'wt':
label = get_whole_tumor_labels(label)
# for whole tumor task, bouding box is self
bbmin = [0, 0, 0]
bbmax = [label.shape[0], label.shape[1], label.shape[2]]
elif self.task_type == 'tc':
# for tumor core task, bounding box is the whole tumor box
no_zero_label = get_whole_tumor_labels(label)
bbmin, bbmax = get_ND_bounding_box(no_zero_label, self.margin)
label = get_tumor_core_labels(label)
# ********** crop image and label based on bounding box **********
for i in range(len(multi_mode_imgs)):
multi_mode_imgs[i] = crop_with_box(multi_mode_imgs[i], bbmin, bbmax, self.data_box)
volume = np.asarray(multi_mode_imgs)
label = crop_with_box(label, bbmin, bbmax, self.data_box) # 3D label
label = label[np.newaxis, :, :, :] # from 3D to 4D label
return volume, label
# test case
if __name__ =="__main__":
vol_num = 4
data_dir = '../data_sample/'
conf = '../config/sample15.conf'
print ('**** whole tumor task *************')
brats15 = Brats15DataLoader(data_dir=data_dir, task_type='wt',
direction='coronal',
conf=conf, is_train=False)
image2d, label2d, index = brats15[60]
print ('image size ......')
print (image2d.shape) # (4, 192, 192)
print ('label size ......')
print (label2d.shape) # (1, 192, 192)
print index
print ('get sample of images')
for i in range(4):
scipy.misc.imsave('img2d/img_%s_wt.jpg' % ddd[i], image2d[i, :, :])
print ('get sample of labels')
scipy.misc.imsave('img2d/label_wt.jpg', label2d[0, :, :]) # 192 * 192