File size: 2,217 Bytes
d4700c7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | # Copyright (c) 2026 Guy's and St Thomas' NHS Foundation Trust & King's College London
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""MONAI transforms for the brain-MRI cohort (MSD Task01_BrainTumour)."""
import json
from pathlib import Path
import monai.transforms as mt
def load_spatial_shape() -> list[int]:
"""Read ``spatial_shape`` from the ``config.json`` next to this file."""
with open(Path(__file__).parent / "config.json", "r") as f:
return list(json.load(f)["spatial_shape"])
SPATIAL_SHAPE = load_spatial_shape()
#: Non-image keys carried through the chain; ``modality_index`` is the latent diffusion condition.
MODALITY_KEYS = ("modality", "modality_index")
def get_brain_mri_transforms(is_validation: bool = False) -> mt.Compose:
"""Return the MONAI transforms used for brain-MRI training/validation.
Args:
is_validation (bool): When True, skip random augmentation.
Returns:
mt.Compose: Composed transform pipeline keyed on "image".
"""
transforms = [
mt.LoadImaged(keys=["image"], image_only=True),
mt.EnsureChannelFirstd(keys=["image"], channel_dim="no_channel"),
mt.Orientationd(keys=["image"], axcodes="RAS"),
mt.Resized(keys=["image"], spatial_size=SPATIAL_SHAPE),
mt.NormalizeIntensityd(keys=["image"], channel_wise=True),
]
if not is_validation:
transforms.append(
mt.RandAffined(
keys=["image"],
rotate_range=(-0.05, 0.05),
scale_range=(0.01, 0.05),
translate_range=(-0.05, 0.05),
prob=1.0,
padding_mode="border",
)
)
return mt.Compose(transforms)
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