feat(ceus,segment): 任务书 02 第二阶段——分割和后半段算法

- ceus 迁入 polygons、dark_region、reference_region、tic_fitting、tic、anchor_frames、 perfusion_metrics、trajectory、tic_curves、tic_plot、overlay，旧测试照搬（改 import 与配置读取）。 - 对外两段接口：prepare_segmentation（第 1–2 步）与 quantify_case（第 4–9 步）。 - 第 3 条：删掉 D1 取医生标注时刻的模式，D1 只从拟合曲线算。 - 第 4 条：F1/F2 取视频第 20−X、60−X 秒（X 为视频第 0 秒时已推注的秒数），落在视频外记未定位。 - 绿框融合权重、紫圈颜色阈值照搬；关键帧容差窗口的出处写进 ceus.yaml 注释。 - segment：同一进程调用 MedSAM2，设置写进 ceus.yaml 的 segment 段，全部必填；gpu 测试真跑通过。 - 依赖加 scipy、matplotlib、medsam2（可编辑安装，不编译 CUDA 扩展）；uv 只解析 linux x86_64。 - 导入契约：只有 segment 引用 torch、sam2；segment 不引用其他 cadx 模块。 Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
2026-10-16 05:07:02 -04:00
parent 9a916c65a5
commit 150eb5f6ae
35 changed files with 10655 additions and 505 deletions
@@ -0,0 +1,230 @@
+"""约束 Lognormal TIC 拟合的纯算法单元测试（照搬旧项目 test_tic_fitting.py）。"""
+
+import numpy as np
+import pytest
+
+from cadx.ceus import (
+    CeusConfig,
+    LognormalParameters,
+    TicFittingConfig,
+    evaluate_lognormal,
+    fit_lognormal_curve,
+)
+from cadx.ceus._tic_fitting import _select_primary_peak  # pyright: ignore[reportPrivateUsage]
+from conftest import load_ceus_section
+
+# 配置不设默认值：旧测试靠默认值构造的配置，改为在 config/ceus.yaml 的取值上 model_copy。
+_CONFIG = CeusConfig.model_validate(load_ceus_section())
+
+
+def _fit(
+    times: np.ndarray,
+    values: np.ndarray,
+    *,
+    duplicates: np.ndarray | None = None,
+    baseline: float | None = 30.0,
+    noise: float | None = 0.2,
+    cfg: TicFittingConfig | None = None,
+):
+    return fit_lognormal_curve(
+        times,
+        values,
+        baseline=baseline,
+        baseline_noise=noise,
+        baseline_end_index=20,
+        duplicate_mask=(np.zeros(times.size, dtype=bool) if duplicates is None else duplicates),
+        sample_mask=None,
+        cfg=cfg or _CONFIG.tic_analysis.fitting,
+        anchor_cfg=_CONFIG.tic_analysis.anchor_frames,
+    )
+
+
+def test_recovers_clean_lognormal_and_continuous_anchors() -> None:
+    times = np.linspace(0.0, 60.0, 601)
+    truth = LognormalParameters(
+        area=400.0,
+        mu=2.2,
+        sigma=0.5,
+        arrival_offset_s=8.0,
+        baseline=30.0,
+    )
+    result = _fit(times, evaluate_lognormal(times, truth))
+
+    assert result.success is True
+    assert result.parameters is not None
+    assert result.r2 == pytest.approx(1.0, abs=1e-8)
+    assert result.parameters.arrival_offset_s == pytest.approx(8.0, abs=0.05)
+    assert result.parameters.sigma == pytest.approx(0.5, abs=0.02)
+    assert result.d1_time_s is not None
+    assert result.d1_time_s < result.d2_time_s < result.d3_time_s < result.d4_time_s  # pyright: ignore[reportOperatorIssue]
+    np.testing.assert_allclose(result.processed, result.fitted)
+
+
+def test_soft_l1_fit_survives_single_compression_spike() -> None:
+    times = np.linspace(0.0, 60.0, 601)
+    truth = LognormalParameters(400.0, 2.2, 0.5, 8.0, 30.0)
+    values = evaluate_lognormal(times, truth)
+    values[180] += 80.0
+
+    result = _fit(times, values)
+
+    assert result.success is True
+    assert result.parameters is not None
+    assert result.d3_time_s == pytest.approx(
+        truth.arrival_offset_s + np.exp(truth.mu - truth.sigma**2), abs=0.25
+    )
+
+
+def test_duplicate_samples_are_excluded_but_keep_original_mapping() -> None:
+    times = np.linspace(0.0, 60.0, 601)
+    truth = LognormalParameters(400.0, 2.2, 0.5, 8.0, 30.0)
+    values = evaluate_lognormal(times, truth)
+    duplicates = np.zeros(times.size, dtype=bool)
+    duplicates[100:102] = True
+
+    result = _fit(times, values, duplicates=duplicates)
+
+    assert result.success is True
+    np.testing.assert_array_equal(result.duplicate_sample_mask, duplicates)
+    assert not result.valid_sample_mask[100:102].any()
+    assert result.processed.shape == values.shape
+
+
+def test_missing_observed_baseline_estimates_prior_but_rejects_flat_curve() -> None:
+    times = np.linspace(0.0, 60.0, 601)
+    values = np.full(times.size, 30.0)
+
+    result = _fit(times, values, baseline=None, noise=None)
+
+    assert result.success is False
+    assert result.failure_reason == "credible_peak_not_found"
+    assert result.baseline_source == "estimated"
+    assert np.isnan(result.processed).all()
+    assert np.isnan(result.fitted).all()
+
+
+def test_left_truncated_curve_uses_estimated_baseline_and_still_fits() -> None:
+    times = np.linspace(0.0, 50.0, 501)
+    truth = LognormalParameters(700.0, 2.7, 0.65, -4.0, 25.0)
+
+    result = _fit(
+        times,
+        evaluate_lognormal(times, truth),
+        baseline=None,
+        noise=None,
+    )
+
+    assert result.success is True
+    assert result.baseline_source == "estimated"
+    assert result.parameters is not None
+    assert result.d3_time_s is not None
+
+
+def test_explicit_sample_mask_excludes_unstable_perf_frame() -> None:
+    times = np.linspace(0.0, 60.0, 601)
+    truth = LognormalParameters(400.0, 2.2, 0.5, 8.0, 30.0)
+    values = evaluate_lognormal(times, truth)
+    values[180] += 100.0
+    sample_mask = np.ones(times.size, dtype=bool)
+    sample_mask[180] = False
+
+    result = fit_lognormal_curve(
+        times,
+        values,
+        baseline=30.0,
+        baseline_noise=0.2,
+        baseline_end_index=20,
+        duplicate_mask=np.zeros(times.size, dtype=bool),
+        sample_mask=sample_mask,
+        cfg=_CONFIG.tic_analysis.fitting,
+        anchor_cfg=_CONFIG.tic_analysis.anchor_frames,
+    )
+
+    assert result.success is True
+    assert result.valid_sample_mask[180] is np.False_
+
+
+def test_quality_failure_keeps_rejected_fit_separate_from_processed() -> None:
+    rng = np.random.default_rng(9)
+    times = np.linspace(0.0, 60.0, 601)
+    truth = LognormalParameters(160.0, 2.0, 0.55, 8.0, 30.0)
+    values = evaluate_lognormal(times, truth) + rng.normal(0.0, 18.0, times.size)
+    cfg = _CONFIG.tic_analysis.fitting.model_copy(update={"minimum_r2": 0.95, "maximum_nrmse": 0.1})
+
+    result = _fit(times, values, noise=1.0, cfg=cfg)
+
+    assert result.success is False
+    assert result.failure_reason in {
+        "fit_quality_failed",
+        "credible_peak_not_found",
+        "model_peak_on_boundary",
+        "parameter_on_boundary",
+    }
+    assert np.isnan(result.processed).all()
+    if result.failure_reason in {
+        "fit_quality_failed",
+        "model_peak_on_boundary",
+        "parameter_on_boundary",
+    }:
+        assert result.parameters is not None
+        assert np.isfinite(result.fitted).all()
+        assert np.isfinite(result.r2)
+        assert np.isfinite(result.nrmse)
+
+
+def test_primary_peak_uses_local_finite_run_and_rejects_single_sample_spike() -> None:
+    grid = np.arange(12.0)
+    candidate = np.asarray([0.0, 0.0, 10.0, 0.0, np.nan, np.nan, 0.0, 2.5, 5.0, 2.5, 0.0, 0.0])
+
+    selected = _select_primary_peak([2, 8], grid, candidate, baseline=0.0)
+
+    assert selected == 8
+
+
+def test_two_boluses_mark_multi_peak_and_fit_first_pass() -> None:
+    times = np.linspace(0.0, 70.0, 701)
+    first = evaluate_lognormal(times, LognormalParameters(300.0, 2.0, 0.4, 5.0, 30.0))
+    second = evaluate_lognormal(times, LognormalParameters(260.0, 1.8, 0.4, 28.0, 0.0))
+    result = _fit(times, first + second)
+
+    assert result.multi_peak_flag is True
+    assert result.fit_end_s < 34.0
+
+
+def test_repeated_shallow_motion_peaks_do_not_truncate_first_bolus() -> None:
+    times = np.linspace(0.0, 70.0, 701)
+    truth = LognormalParameters(400.0, 2.4, 0.75, 5.0, 30.0)
+    broad_bolus = evaluate_lognormal(times, truth)
+    motion = np.where(times > 8.0, 2.0 * np.sin(times * 2.5), 0.0)
+
+    result = _fit(times, broad_bolus + motion)
+
+    assert result.multi_peak_flag is False
+    assert result.fit_end_s == pytest.approx(times[-1])
+    assert result.success is True
+
+
+def test_truncated_washout_does_not_use_fit_boundary_as_d4() -> None:
+    full_times = np.linspace(0.0, 60.0, 601)
+    truth = LognormalParameters(400.0, 2.2, 0.5, 8.0, 30.0)
+    full_values = evaluate_lognormal(full_times, truth)
+    keep = full_times <= 17.0
+
+    result = _fit(full_times[keep], full_values[keep])
+
+    assert result.success is True
+    assert result.d3_time_s is not None
+    assert result.d4_time_s is None
+
+
+def test_narrow_bolus_keeps_analytic_d4_in_long_video() -> None:
+    times = np.linspace(0.0, 90.0, 901)
+    truth = LognormalParameters(400.0, 2.2, 0.15, 8.0, 30.0)
+
+    result = _fit(times, evaluate_lognormal(times, truth))
+
+    sigma_squared = truth.sigma**2
+    wash_out_factor = (sigma_squared - np.sqrt(sigma_squared**2 + 4.0 * sigma_squared)) / 2.0
+    expected_d4 = truth.arrival_offset_s + np.exp(truth.mu - sigma_squared - wash_out_factor)
+    assert result.success is True
+    assert result.d4_time_s == pytest.approx(expected_d4, abs=0.05)