Average-Calibration-Losses/bundle_templates/configs/data/kits23.yaml at main · cai4cai/Average-Calibration-Losses · GitHub

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# Target: Kidney, kidney tumor, and kidney cyst
# Modality: CT
# Size: 599 cases (489 Training, 110 Testing)
# Source: M Health Fairview medical center
# Challenge: Segmentation of kidney, tumor, and cyst from contrast-enhanced preoperative CT scans (2010-2022)

num_channels: 1
num_classes: 4

cache_size: 96  # Lower cache size to limit RAM usage of such massive images ~[512, 512, 512]
num_epochs: 1250

patch_size: [128, 128, 128]
val_windows_size: [256, 256, 256]
resample_resolution: [1.0, 1.0, 1.0]

# class_names: $['background', 'kidney', 'tumour', 'cyst']  # Original classes
class_names: $['background', 'tumour', 'kidney_mass', 'kindey_and_masses']  # HEC classes

batch_name_transform: $src.utils.meta_data_batch_transform_dir  # case name in is directory
image_name_transform: $src.utils.meta_data_image_transform_dir

##########################
# DATASET SETUP
##########################
dataset_dir: '../data/kits23/dataset'
test_seed: 42
test_split_ratio: 0.2

all_cases: '$sorted(glob.glob(os.path.join(@dataset_dir, "case_*")))'
all_images: '$[os.path.join(case, "imaging.nii.gz") for case in @all_cases]'
all_labels: '$[os.path.join(case, "segmentation.nii.gz") for case in @all_cases]'
all_dicts: "$[{'image': img, 'label': lbl} for img, lbl in zip(@all_images, @all_labels)]"

# Split the all_dicts into training and testing subsets
partitions_train_test: "$monai.data.partition_dataset(@all_dicts, (0.8, 0.2), shuffle=True, seed=@test_seed)"
data_dicts: "$@partitions_train_test[0]"  # used for training and validation
test_sub: "$@partitions_train_test[1]"  # used as the hold out test set

partitions: '$monai.data.partition_dataset(@data_dicts, (9, 1), shuffle=True, seed=@seed)'
train_sub: '$@partitions[0]'
val_sub: '$@partitions[1]'

##########################
# PRE-PROCESSING TRANSFORMATIONS
##########################

load_transforms:
  - _target_: LoadImaged
    keys: ['image', 'label']
  - _target_: EnsureChannelFirstd
    keys: ['image', 'label']
  - _target_: EnsureTyped
    keys: ['image', 'label']
  - _target_: EnsureSameShaped
    keys: ['label']
    source_key: 'image'
    allow_missing_keys: true
    warn: '@debug'

resample_transforms:
  - _target_: CropForegroundd
    keys: ['image', 'label']
    source_key: 'image'
  - _target_: Orientationd
    keys: ['image', 'label']
    axcodes: 'RAS'
  - _target_: Spacingd
    keys: ['image', 'label']
    pixdim: '@resample_resolution'
    mode: ['bilinear', 'nearest']

normalize_transforms:
  - _target_: ScaleIntensityRanged
    keys: ['image']
    a_min: -54
    a_max: 242
    b_min: 0
    b_max: 1.0
    clip: true

augmentation_transforms:
  - _target_: RandAffined
    keys: ['image', 'label']
    prob: 0.5
    rotate_range: [0.1, 0.1, 0.1]
    shear_range: [0.1, 0.1, 0.1]
    translate_range: [0.1, 0.1, 0.1]
    scale_range: [0.1, 0.1, 0.1]
    mode: ['bilinear', 'nearest']
    padding_mode: ['constant', 'border']
  # - _target_: Rand3DElasticd
  #   keys: ['image', 'label']
  #   sigma_range: [1, 2]
  #   magnitude_range: [0, 0.2]
  #   prob: 0.5
  #   rotate_range: [0.1, 0.1, 0.1]
  #   shear_range: [0.1, 0.1, 0.1]
  #   translate_range: [0.1, 0.1, 0.1]
  #   scale_range: [0.1, 0.1, 0.1]
  #   mode: ['bilinear', 'nearest']

crop_transforms:
  - _target_: RandCropByPosNegLabeld
    keys: ['image', 'label']
    label_key: 'label'
    spatial_size: '@patch_size'
    pos: 1
    neg: 1
    num_samples: 4
    image_key: 'image'
    image_threshold: 0

train_transforms:
  - _target_: Compose
    transforms: $@load_transforms + @resample_transforms + @normalize_transforms + @augmentation_transforms + @crop_transforms

val_transforms:
  _target_: Compose
  transforms: $@load_transforms + @resample_transforms + @normalize_transforms

##########################
# POST-PROCESSING AND INFERENCE
##########################
# postprocessing:  # This results in the loss being calculated on the HEC classes
#   _target_: Compose
#   transforms:
#   - _target_: src.transforms.ConvertToKits23Classesd  # labelmap to discrete "one-hot" kits classes
#     keys: '@label'
#   - _target_: src.transforms.ConvertToKits23ClassesSoftmaxd  # softmax predicion to combined softmax kits classes
#     keys: '@pred'

val_postprocessing:   # validation and inference with HEC classes
  _target_: Compose
  transforms:
  - _target_: Activationsd  # softmax prediction
    keys: '@pred'
    softmax: true
  - _target_: src.transforms.ConvertToKits23Classesd
    keys: '@label'
  - _target_: src.transforms.ConvertToKits23ClassesSoftmaxd
    keys: '@pred'

# val_postprocessing:  # validation and inference without HEC classes
#   _target_: Compose
#   transforms:
#   - _target_: Activationsd
#     keys: '@pred'
#     softmax: true
#   - _target_: AsDiscreted
#     keys: '@label'
#     to_onehot: '@num_classes'