MMs-Cardiac-Dataset-with-only-ED-and-ES
MMs Cardiac Dataset with only ED (end-diastole) and ES (end of systole). The ground truth is labeled cardiac and the volume of each cardiac structure.
Motivation
This dataset is adpated from Multi-Centre, Multi-Vendor & Multi-Disease
Cardiac Image Segmentation Challenge (M&Ms). The compressed original dataset has 14.3 GB. The shape of each subject/cardiac is (n, z, y, x).
n: number of frames;
z: number of slices;
x,y: 2D size.
This repo aims to share a preprocessed dataset that only has ED and ES frame. Besides, the volume of each cardiac structure (RV, MYO, LV) is given in the file.
Method
ED and ES extraction
The ED and ES number are given in the original CSV file.
def ExtractPhase(ed=5, es=5, subject=None, ED=None, ES=None):
itk_img = sitk.ReadImage(subject)
img = sitk.GetArrayFromImage(itk_img)
print('The shape of this subject:', img.shape) # frames, slices, y,x
for i in range(img.shape[0]):
if i == ed:
img_ed = sitk.GetImageFromArray(img[i])
sitk.WriteImage(img_ed, ED)
if i == es:
img_es = sitk.GetImageFromArray(img[i])
sitk.WriteImage(img_es, ES)
Volume calculation
def VolumeStat(image_path):
imagelist = sorted(glob.glob(os.path.join(image_path, '*.nii.gz')))
for i in range(len(imagelist)):
itk_img = sitk.ReadImage(imagelist[i])
img = sitk.GetArrayFromImage(itk_img)
pixel_count_BKG = 0
pixel_count_LV = 0
pixel_count_MYO = 0
pixel_count_RV = 0
LV = 1
MYO = 2
RV = 3
for k in range(img.shape[0]):
pixel_count_RV += (img[k] == RV).sum()
pixel_count_MYO += (img[k] == MYO).sum()
pixel_count_LV += (img[k] == LV).sum()
pixel number convert to volume (milliter)
Vol_RV = pixelsize_x*pixelsize_y*pixelsize_z*pixel_count_RV/1000
The pixelsize can be obtained by itk_img.GetSpacing()
Download
You can download the preprocessed data from the link below:
Dropbox
