Understanding Task01_BrainTumour Dataset and Image Labels Retrieval

[agokli]

Behind the scene I will like to understand how images and labels are retrieved from nifti files from imagesTr and labelsTr folders. Various turtorials presents logics: (an example from Weakly Supervised Anomaly Detection with Implicit Guidance)

channel = 0 # 0 = Flair
assert channel in [0, 1, 2, 3], "Choose a valid channel"

train_transforms = transforms.Compose(
[
transforms.LoadImaged(keys=["image", "label"]),
transforms.EnsureChannelFirstd(keys=["image", "label"]),
transforms.Lambdad(keys=["image"], func=lambda x: x[channel, :, :, :]),
transforms.EnsureChannelFirstd(channel_dim="no_channel", keys=["image"]),
transforms.EnsureTyped(keys=["image", "label"]),
transforms.Orientationd(keys=["image", "label"], axcodes="RAS"),
transforms.Spacingd(keys=["image", "label"], pixdim=(3.0, 3.0, 2.0), mode=("bilinear", "nearest")),
transforms.CenterSpatialCropd(keys=["image", "label"], roi_size=(64, 64, 44)),
transforms.ScaleIntensityRangePercentilesd(keys="image", lower=0, upper=99.5, b_min=0, b_max=1),
transforms.RandSpatialCropd(keys=["image", "label"], roi_size=(64, 64, 1), random_size=False),
transforms.Lambdad(keys=["image", "label"], func=lambda x: x.squeeze(-1)),
transforms.CopyItemsd(keys=["label"], times=1, names=["slice_label"]),
transforms.Lambdad(keys=["slice_label"], func=lambda x: 2.0 if x.sum() > 0 else 1.0),
]
)
train_ds = DecathlonDataset(
root_dir=root_dir,
task="Task01_BrainTumour",
section="training",
cache_rate=1.0, # you may need a few Gb of RAM... Set to 0 otherwise
num_workers=4,
download=True, # Set download to True if the dataset hasn't been downloaded yet
seed=0,
transform=train_transforms)

train_loader = DataLoader(
train_ds, batch_size=batch_size, shuffle=True, num_workers=4, drop_last=True, persistent_workers=True
)

while iteration < max_epochs:
for batch in train_loader:
iteration += 1
model.train()
images, classes = batch["image"].to(device), batch["slice_label"].to(device)
.. . .. ...

Questions:

(a) The images and labels in nifti format (imagesTr and labelsTr folders). Behind scene, how are images and labels are retrieved from those nifiti files?

(b) The statement transforms.Lambdad(keys=["slice_label"], func=lambda x: 2.0 if x.sum() > 0 else 1.0) retrives slice_label values accordingly (conditionally). So, what are these sums within underneath logic?

(c) I really want to use other BRATS data , where images and labels are stored as nifit files and then use MONAI transforms.

Can you please provide possible solution(s) or pointer(s) or tutorials?

Thanks

Avinash Gokli