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"""Metrics for evaluating segmentation quality."""
from __future__ import annotations
import math
from pathlib import Path
from typing import TYPE_CHECKING, Any
import nibabel as nib
import numpy as np
if TYPE_CHECKING:
from numpy.typing import NDArray
def load_nifti_as_array(path: Path) -> tuple[NDArray[np.floating[Any]], tuple[float, float, float]]:
"""
Load NIfTI file and return data array with voxel dimensions.
Args:
path: Path to NIfTI file
Returns:
Tuple of (data_array, voxel_sizes_mm)
"""
img = nib.load(path) # type: ignore[attr-defined]
# Use float32 for memory efficiency (sufficient for medical images)
data = img.get_fdata(dtype=np.float32) # type: ignore[attr-defined]
zooms = img.header.get_zooms() # type: ignore[attr-defined]
# zooms can be 3D or 4D, we want spatial dims. DeepISLES output is 3D.
# Extract exactly 3 spatial dimensions.
spatial_zooms = zooms[:3]
voxel_sizes: tuple[float, float, float] = (
float(spatial_zooms[0]),
float(spatial_zooms[1]),
float(spatial_zooms[2]),
)
return data, voxel_sizes
def compute_dice(
prediction: Path | NDArray[np.floating[Any]],
ground_truth: Path | NDArray[np.floating[Any]],
*,
threshold: float = 0.5,
) -> float:
"""
Compute Dice similarity coefficient between prediction and ground truth.
Dice = 2 * |P ∩ G| / (|P| + |G|)
Args:
prediction: Path to NIfTI file or numpy array
ground_truth: Path to NIfTI file or numpy array
threshold: Threshold for binarization (if needed)
Returns:
Dice coefficient in [0, 1]
Raises:
ValueError: If shapes don't match
"""
if isinstance(prediction, Path):
p_data, _ = load_nifti_as_array(prediction)
else:
p_data = prediction
if isinstance(ground_truth, Path):
g_data, _ = load_nifti_as_array(ground_truth)
else:
g_data = ground_truth
if p_data.shape != g_data.shape:
raise ValueError(
f"Shape mismatch: prediction {p_data.shape} vs ground truth {g_data.shape}"
)
# Binarize
p_bin = (p_data > threshold).astype(bool)
g_bin = (g_data > threshold).astype(bool)
intersection = np.sum(p_bin & g_bin)
total = np.sum(p_bin) + np.sum(g_bin)
if total == 0:
return 1.0 # Both empty
return float(2.0 * intersection / total)
def compute_volume_ml(
mask: Path | NDArray[np.floating[Any]],
voxel_size_mm: tuple[float, float, float] | None = None,
*,
threshold: float = 0.5,
) -> float:
"""
Compute lesion volume in milliliters.
Args:
mask: Path to NIfTI file or numpy array
voxel_size_mm: Voxel dimensions in mm (read from NIfTI if None)
threshold: Threshold for binarization (default 0.5 for consistency with compute_dice)
Returns:
Volume in milliliters (mL)
Note:
Uses the same default threshold (0.5) as compute_dice for consistency.
This ensures the volume measurement matches the clinical segmentation decision boundary.
"""
if isinstance(mask, Path):
data, loaded_zooms = load_nifti_as_array(mask)
voxel_dims = voxel_size_mm if voxel_size_mm is not None else loaded_zooms
else:
data = mask
# Default to 1mm isotropic if not provided for array
voxel_dims = voxel_size_mm if voxel_size_mm is not None else (1.0, 1.0, 1.0)
# Binarize at threshold for consistent measurement with compute_dice
volume_voxels = np.sum(data > threshold)
voxel_vol_mm3 = math.prod(voxel_dims)
return float(volume_voxels * voxel_vol_mm3 / 1000.0) # mm3 -> mL
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