{
  "name": "medtrace-brats-seg",
  "version": "1.0.0",
  "task": "3D brain tumour segmentation from four MRI sequences",
  "intended_use": "Produce tumour compartment masks for MEDTRACE's longitudinal measurement pipeline. Research use. Not a diagnostic device and not for clinical decision-making.",
  "architecture": {
    "class": "monai.networks.nets.SegResNet",
    "spatial_dims": 3,
    "in_channels": 4,
    "out_channels": 3,
    "init_filters": 32,
    "blocks_down": [
      1,
      2,
      2,
      4
    ],
    "blocks_up": [
      1,
      1,
      1
    ],
    "dropout_prob": 0.2,
    "parameters": 18798627
  },
  "input_contract": {
    "channel_order": [
      "t1c",
      "t1n",
      "t2f",
      "t2w"
    ],
    "note": "Channel order is not recoverable from the weights. Wrong order, wrong output, no error.",
    "normalisation": "per case, per channel, zero mean unit variance over NON-ZERO voxels only",
    "spacing_mm": [
      1.0,
      1.0,
      1.0
    ],
    "orientation": "as distributed by BraTS 2023 (already co-registered and skull-stripped)",
    "cropping": "crop to the non-zero bounding box of the summed channels, 4 voxel margin",
    "patch_size": [
      128,
      128,
      128
    ],
    "inference": {
      "method": "sliding window",
      "overlap": 0.5,
      "blend": "gaussian"
    }
  },
  "output_contract": {
    "activation": "sigmoid, independent per channel",
    "channels": [
      "TC",
      "WT",
      "ET"
    ],
    "channel_meaning": {
      "TC": "tumour core = necrotic core + enhancing",
      "WT": "whole tumour = necrotic core + oedema + enhancing",
      "ET": "enhancing tumour"
    },
    "overlapping": true,
    "postprocessing": {
      "threshold": 0.5,
      "min_component_voxels": 0,
      "et_min_voxels": 200
    },
    "to_integer_labels": {
      "convention": "BRATS",
      "mapping": {
        "1": "necrotic_core",
        "2": "oedema",
        "3": "enhancing"
      },
      "procedure": "write WT as 2, then TC as 1, then ET as 3, in that order"
    }
  },
  "training_data": {
    "dataset": "BraTS 2023 GLI, challenge training split (the only labelled split)",
    "split_unit": "subject",
    "split_seed": 20260813,
    "cases": {
      "train": 874,
      "val": 191,
      "test": 186
    },
    "subjects": {
      "train": 792,
      "val": 172,
      "test": 169
    },
    "label_mapping_verified": true,
    "excluded_cases": []
  },
  "training": {
    "epochs_completed": 37,
    "epochs_planned": 60,
    "selected_epoch": 32,
    "selection_metric": "mean Dice over TC, WT, ET on the validation split",
    "loss": "DiceFocalLoss(sigmoid=True, squared_pred=True, batch=True)",
    "optimizer": "AdamW lr=0.0002 wd=1e-05",
    "scheduler": "CosineAnnealingLR",
    "precision": "AMP float16",
    "augmentation": [
      "random axis flips",
      "intensity scale +-10%",
      "intensity shift +-10%"
    ],
    "patch_sampling": "80% centred on whole tumour, 20% uniform",
    "seed": 20260813
  },
  "metrics": {
    "validation": {
      "TC": {
        "dice_mean": 0.9015418441673284,
        "dice_median": 0.947789848286196,
        "hd95_median": 2.0
      },
      "WT": {
        "dice_mean": 0.9177529638232264,
        "dice_median": 0.939965202760836,
        "hd95_median": 3.316624879837036
      },
      "ET": {
        "dice_mean": 0.8503461605067909,
        "dice_median": 0.9047415373334963,
        "hd95_median": 1.4142135381698608
      }
    },
    "test": {
      "TC": {
        "dice_mean": 0.9078361539305267,
        "dice_median": 0.9563434942782733,
        "hd95_median": 2.0,
        "sensitivity_mean": 0.9165695598827129,
        "precision_mean": 0.9170155149790782
      },
      "WT": {
        "dice_mean": 0.921562897191002,
        "dice_median": 0.9489419374016819,
        "hd95_median": 2.4494898319244385,
        "sensitivity_mean": 0.9247750225794058,
        "precision_mean": 0.9235932925559869
      },
      "ET": {
        "dice_mean": 0.852042452735223,
        "dice_median": 0.8984435921432055,
        "hd95_median": 1.4142135381698608,
        "sensitivity_mean": 0.8833305802745401,
        "precision_mean": 0.8486887298796273
      }
    },
    "empty_region_rule": "BraTS convention: with empty ground truth, Dice is 1 if the prediction is also empty and 0 otherwise. Not averaged away as NaN."
  },
  "limitations": [
    "Trained on pre-operative adult glioma only. LUMIERE's post-treatment appearances, including resection cavities and radiation change, are not represented.",
    "Requires all four sequences. Behaviour with a missing sequence is untested.",
    "Assumes BraTS preprocessing: skull-stripped, co-registered, 1 mm isotropic.",
    "Agreement with one annotation protocol on one dataset. Not a measure of clinical accuracy.",
    "Selection and post-processing tuning both used the validation split, so validation figures are optimistic. Use the test figures."
  ],
  "environment": {
    "monai": "1.6.0",
    "torch": "2.10.0+cu128",
    "numpy": "2.0.2",
    "device": "Tesla T4, 15.6 GB",
    "amp": true
  }
}