124 lines
3.7 KiB
Python
124 lines
3.7 KiB
Python
"""配准质量评估工具。
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这里刻意只依赖 NumPy，便于在训练、推理和 Streamlit 三处复用。
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"""
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from __future__ import annotations
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from typing import Dict, Iterable, Tuple
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import numpy as np
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def crop_to_common_shape(*arrays: np.ndarray) -> Tuple[np.ndarray, ...]:
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"""把多个 3D/4D 数组中心裁剪到共同尺寸，避免形状不一致导致评估失败。"""
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if not arrays:
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return tuple()
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spatial_shapes = [arr.shape[:3] for arr in arrays]
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common = tuple(min(shape[axis] for shape in spatial_shapes) for axis in range(3))
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cropped = []
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for arr in arrays:
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slices = []
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for axis, target in enumerate(common):
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start = max((arr.shape[axis] - target) // 2, 0)
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slices.append(slice(start, start + target))
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if arr.ndim > 3:
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slices.append(slice(None))
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cropped.append(arr[tuple(slices)])
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return tuple(cropped)
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def global_ncc(a: np.ndarray, b: np.ndarray, eps: float = 1e-8) -> float:
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"""计算全局 NCC。训练时使用局部 NCC，这里用于快速量化展示。"""
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a = np.asarray(a, dtype=np.float32)
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b = np.asarray(b, dtype=np.float32)
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a = a - float(np.mean(a))
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b = b - float(np.mean(b))
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denom = float(np.sqrt(np.sum(a * a) * np.sum(b * b)) + eps)
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return float(np.sum(a * b) / denom)
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def mse(a: np.ndarray, b: np.ndarray) -> float:
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diff = np.asarray(a, dtype=np.float32) - np.asarray(b, dtype=np.float32)
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return float(np.mean(diff * diff))
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def mae(a: np.ndarray, b: np.ndarray) -> float:
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diff = np.abs(np.asarray(a, dtype=np.float32) - np.asarray(b, dtype=np.float32))
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return float(np.mean(diff))
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def registration_metrics(
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fixed: np.ndarray,
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moving: np.ndarray,
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warped: np.ndarray,
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) -> Dict[str, float]:
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"""输出配准前后可比较的常用指标。"""
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fixed, moving, warped = crop_to_common_shape(fixed, moving, warped)
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before_mse = mse(fixed, moving)
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after_mse = mse(fixed, warped)
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before_mae = mae(fixed, moving)
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after_mae = mae(fixed, warped)
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before_ncc = global_ncc(fixed, moving)
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after_ncc = global_ncc(fixed, warped)
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return {
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"before_mse": before_mse,
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"after_mse": after_mse,
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"before_mae": before_mae,
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"after_mae": after_mae,
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"before_ncc": before_ncc,
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"after_ncc": after_ncc,
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"mse_improvement": before_mse - after_mse,
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"mae_improvement": before_mae - after_mae,
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"ncc_improvement": after_ncc - before_ncc,
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}
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def slice_metric_curve(
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fixed: np.ndarray,
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moving: np.ndarray,
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warped: np.ndarray,
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axis: int = 2,
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) -> Dict[str, Iterable[float]]:
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"""逐切片计算 MSE，适合生成“配准前后误差曲线”。"""
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fixed, moving, warped = crop_to_common_shape(fixed, moving, warped)
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before = []
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after = []
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for index in range(fixed.shape[axis]):
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selector = [slice(None)] * 3
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selector[axis] = index
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selector = tuple(selector)
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before.append(mse(fixed[selector], moving[selector]))
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after.append(mse(fixed[selector], warped[selector]))
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return {
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"slice_index": list(range(fixed.shape[axis])),
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"before_mse": before,
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"after_mse": after,
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}
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def ddf_summary(ddf_xyz: np.ndarray) -> Dict[str, float]:
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"""统计形变场向量大小。输入应为 X/Y/Z/3，单位可为 voxel 或 mm。"""
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if ddf_xyz.ndim != 4 or ddf_xyz.shape[-1] != 3:
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raise ValueError("DDF 必须是形状为 (X, Y, Z, 3) 的 4D 数组。")
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mag = np.linalg.norm(ddf_xyz.astype(np.float32), axis=-1)
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return {
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"ddf_mean": float(np.mean(mag)),
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"ddf_std": float(np.std(mag)),
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"ddf_p95": float(np.percentile(mag, 95)),
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"ddf_max": float(np.max(mag)),
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}
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