File size: 6,224 Bytes
d5d23f9 | 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 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 | """Public mask-to-contour post-processing functions."""
from __future__ import annotations
import numpy as np
from .geometry import (
largest_external_contour,
myocardium_ring_boundaries,
physical_to_xy,
render_periodic_bspline,
render_tension_curve,
select_adaptive_control_indices,
select_sparse_spline_controls,
xy_to_physical,
)
from .metrics import contour_quality_metrics
from .presets import LAX_EDITABLE_CONTOUR_V1
from .types import (
AdaptiveContourConfig,
ContourConfig,
MaskToContourResult,
MyocardiumContourResult,
PixelSpacing,
SplineContourConfig,
validate_pixel_spacing,
)
def _convert_dense_contour(
dense_xy: np.ndarray,
shape: tuple[int, int],
pixel_spacing: PixelSpacing,
config: ContourConfig,
) -> MaskToContourResult:
physical_dense = xy_to_physical(dense_xy, pixel_spacing)
if isinstance(config, AdaptiveContourConfig):
indices = select_adaptive_control_indices(physical_dense, config)
physical_control = physical_dense[indices]
physical_smooth = render_tension_curve(
physical_control,
tension=config.tension,
samples_per_segment=config.samples_per_segment,
)
control_xy = dense_xy[indices].astype(np.float64)
elif isinstance(config, SplineContourConfig):
config.validate()
physical_bspline = render_periodic_bspline(physical_dense, config)
physical_control, physical_smooth = select_sparse_spline_controls(
physical_bspline, config
)
control_xy = physical_to_xy(physical_control, pixel_spacing)
else: # pragma: no cover - protected by the public type and explicit error
raise TypeError(f"unsupported contour config: {type(config).__name__}")
smooth_xy = physical_to_xy(physical_smooth, pixel_spacing)
metrics, rendered_mask = contour_quality_metrics(
dense_xy,
smooth_xy,
shape,
pixel_spacing,
control_point_count=len(control_xy),
)
return MaskToContourResult(
dense_contour=dense_xy.astype(np.float64),
control_points=control_xy,
smooth_contour=smooth_xy,
rendered_mask=rendered_mask,
metrics=metrics,
pixel_spacing=pixel_spacing,
preset_name=config.name,
)
def mask_to_smooth_contour(
mask: np.ndarray,
pixel_spacing: PixelSpacing | None = None,
config: ContourConfig = LAX_EDITABLE_CONTOUR_V1,
) -> MaskToContourResult:
"""Convert the largest mask component to a smooth closed contour."""
spacing = validate_pixel_spacing(pixel_spacing)
dense_xy = largest_external_contour(mask)
return _convert_dense_contour(dense_xy, np.asarray(mask).shape, spacing, config)
def myocardium_mask_to_smooth_contours(
mask: np.ndarray,
pixel_spacing: PixelSpacing | None = None,
config: ContourConfig = LAX_EDITABLE_CONTOUR_V1,
) -> MyocardiumContourResult:
"""Convert a myocardium ring mask into paired endo/epi smooth contours."""
spacing = validate_pixel_spacing(pixel_spacing)
array = np.asarray(mask)
inner, outer = myocardium_ring_boundaries(array)
endocardium = _convert_dense_contour(inner, array.shape, spacing, config)
epicardium = _convert_dense_contour(outer, array.shape, spacing, config)
rendered = np.logical_and(
epicardium.rendered_mask > 0, endocardium.rendered_mask == 0
).astype(np.uint8)
source = array > 0
intersection = int(np.logical_and(source, rendered > 0).sum())
union = int(np.logical_or(source, rendered > 0).sum())
source_area = int(source.sum())
rendered_area = int(rendered.sum())
return MyocardiumContourResult(
endocardium=endocardium,
epicardium=epicardium,
rendered_myocardium_mask=rendered,
mask_iou=float(intersection / union) if union else 0.0,
area_change_pct=(
float(100.0 * (rendered_area - source_area) / source_area)
if source_area
else float("inf")
),
)
def cavity_myocardium_masks_to_smooth_contours(
cavity_mask: np.ndarray,
myocardium_mask: np.ndarray,
pixel_spacing: PixelSpacing | None = None,
config: ContourConfig = LAX_EDITABLE_CONTOUR_V1,
) -> MyocardiumContourResult:
"""Convert cavity and myocardium predictions to endo/epi contours.
The endocardium is the cavity's largest external boundary. The
epicardium is the largest external boundary of ``cavity | myocardium``.
This supports model outputs where the myocardium class is either a ring or
an overlapping/filled epicardial region.
"""
spacing = validate_pixel_spacing(pixel_spacing)
cavity = np.asarray(cavity_mask)
myocardium = np.asarray(myocardium_mask)
if cavity.ndim != 2 or myocardium.ndim != 2:
raise ValueError("cavity_mask and myocardium_mask must both be 2D")
if cavity.shape != myocardium.shape:
raise ValueError(
"cavity_mask and myocardium_mask must have identical shapes"
)
cavity_binary = cavity > 0
myocardium_binary = myocardium > 0
outer_binary = np.logical_or(cavity_binary, myocardium_binary)
endocardium = _convert_dense_contour(
largest_external_contour(cavity_binary), cavity.shape, spacing, config
)
epicardium = _convert_dense_contour(
largest_external_contour(outer_binary), cavity.shape, spacing, config
)
rendered = np.logical_and(
epicardium.rendered_mask > 0, endocardium.rendered_mask == 0
).astype(np.uint8)
source = np.logical_and(outer_binary, np.logical_not(cavity_binary))
intersection = int(np.logical_and(source, rendered > 0).sum())
union = int(np.logical_or(source, rendered > 0).sum())
source_area = int(source.sum())
rendered_area = int(rendered.sum())
return MyocardiumContourResult(
endocardium=endocardium,
epicardium=epicardium,
rendered_myocardium_mask=rendered,
mask_iou=float(intersection / union) if union else 0.0,
area_change_pct=(
float(100.0 * (rendered_area - source_area) / source_area)
if source_area
else float("inf")
),
)
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