File size: 2,477 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 61 62 | # 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_config() -> dict:
"""Read the ``config.json`` next to this file."""
with open(Path(__file__).parent / "config.json", "r") as f:
return json.load(f)
#: The grid every volume is cropped or padded to, after resampling to ``VOXEL_SPACING`` millimetres.
#: Task01's scans are 240x240x155 mm; at 2 mm, 96x112x80 holds every brain in it whole (the largest is
#: 160x187x149 mm, and brains sit up to 20 mm off-centre), where a 96^3 grid would cut 95 of 484.
SPATIAL_SHAPE = list(load_config()["spatial_shape"])
VOXEL_SPACING = list(load_config()["voxel_spacing"])
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"),
# v1.1 (bitacora): v1's squash geometry, the whole scan resized straight to the grid.
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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