Merge pull request #40 from computational-cell-analytics/gfp-annotation

Add script for gfp intensity analysis

Author: constantinpape

scripts/intensity_annotation/gfp_annotation.py

@@ -0,0 +1,202 @@
+import argparse
+
+import imageio.v3 as imageio
+import napari
+import numpy as np
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+from qtpy.QtWidgets import QWidget, QVBoxLayout, QPushButton, QLabel, QComboBox
+from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg
+from magicgui import magicgui
+
+from elf.parallel.distance_transform import distance_transform
+from elf.parallel.seeded_watershed import seeded_watershed
+
+from flamingo_tools.measurements import compute_object_measures_impl
+
+
+class HistogramWidget(QWidget):
+    """Qt widget that draws/updates a histogram for one napari layer."""
+    def __init__(self, statistics, default_stat, bins: int = 32, parent=None):
+        super().__init__(parent)
+        self.bins = bins
+
+        # --- layout ------------------------------------------------------
+        self.fig, self.ax = plt.subplots(figsize=(4, 3), tight_layout=True)
+        self.canvas = FigureCanvasQTAgg(self.fig)
+
+        # We exclude the label id and the volume / surface measurements.
+        self.stat_names = statistics.columns[1:-2]
+        self.param_choices = self.stat_names
+
+        self.param_box = QComboBox()
+        self.param_box.addItems(self.param_choices)
+        self.param_box.setCurrentText(default_stat)
+
+        self.refresh_btn = QPushButton("Refresh")
+        self.refresh_btn.clicked.connect(self.update_hist)
+
+        layout = QVBoxLayout()
+        layout.addWidget(QLabel("Choose statistic:"))
+        layout.addWidget(self.param_box)
+        layout.addWidget(self.canvas)
+        layout.addWidget(self.refresh_btn)
+        self.setLayout(layout)
+
+        self.statistics = statistics
+        self.update_hist()  # initial draw
+
+    def update_hist(self):
+        """Redraw the histogram."""
+        self.ax.clear()
+
+        stat_name = self.param_box.currentText()
+
+        data = self.statistics[stat_name]
+        # Seaborn version (nicer aesthetics)
+        sns.histplot(data, bins=self.bins, ax=self.ax, kde=False)
+
+        self.ax.set_xlabel(f"{stat_name} GFP Intensity")
+        self.ax.set_ylabel("Count")
+        self.canvas.draw_idle()
+
+
+def _create_stat_widget(statistics, default_stat):
+    widget = HistogramWidget(statistics, default_stat)
+    return widget
+
+
+# Extend via watershed, this could work for a better alignment.
+def _extend_sgns_complex(gfp, sgns):
+    # 1.) compute distance to the SGNs to create the background seed.
+    print("Compute edt")
+
+    # Could use parallel impl
+    distance_threshol = 7
+    distances = distance_transform(sgns == 0)
+
+    # Erode seeds?
+    seeds = sgns.copy()
+    bg_seed_id = int(seeds.max()) + 1
+    seeds[distances > distance_threshol] = bg_seed_id
+
+    # Dilate to cover everything on the boundary?
+    print("Run watershed")
+    sgns_extended = seeded_watershed(gfp, markers=seeds)
+    sgns_extended[sgns_extended == bg_seed_id] = 0
+
+    v = napari.Viewer()
+    v.add_image(gfp)
+    v.add_image(distances)
+    v.add_labels(seeds)
+    v.add_labels(sgns_extended)
+    napari.run()
+
+
+# Just dilate by 3 pixels.
+def _extend_sgns_simple(gfp, sgns, dilation):
+    block_shape = (128,) * 3
+    halo = (dilation + 2,) * 3
+
+    distances = distance_transform(sgns == 0, block_shape=block_shape, halo=halo, n_threads=8)
+    mask = distances < dilation
+
+    sgns_extended = np.zeros_like(sgns)
+    sgns_extended = seeded_watershed(
+        distances, sgns, sgns_extended, block_shape=block_shape, halo=halo, n_threads=8, mask=mask
+    )
+
+    return sgns_extended
+
+
+def gfp_annotation(prefix, default_stat="mean"):
+    gfp = imageio.imread(f"{prefix}_GFP_resized.tif")
+    sgns = imageio.imread(f"{prefix}_SGN_resized_v2.tif")
+    pv = imageio.imread(f"{prefix}_PV_resized.tif")
+
+    # bb = np.s_[128:-128, 128:-128, 128:-128]
+    # gfp, sgns, pv = gfp[bb], sgns[bb], pv[bb]
+    # print(gfp.shape)
+
+    # Extend the sgns so that they cover the SGN boundaries.
+    # sgns_extended = _extend_sgns(gfp, sgns)
+    # TODO we need to integrate this directly in the object measurement to efficiently do it at scale.
+    sgns_extended = _extend_sgns_simple(gfp, sgns, dilation=4)
+
+    # Compute the intensity statistics.
+    statistics = compute_object_measures_impl(gfp, sgns_extended)
+
+    # Open the napari viewer.
+    v = napari.Viewer()
+
+    # Add the base layers.
+    v.add_image(gfp, name="GFP")
+    v.add_image(pv, visible=False, name="PV")
+    v.add_labels(sgns, visible=False, name="SGNs")
+    v.add_labels(sgns_extended, name="SGNs-extended")
+
+    # Add additional layers for intensity coloring and classification
+    # data_numerical = np.zeros(gfp.shape, dtype="float32")
+    data_labels = np.zeros(gfp.shape, dtype="uint8")
+
+    # v.add_image(data_numerical, name="gfp-intensity")
+    v.add_labels(data_labels, name="positive-negative")
+
+    # Add widgets:
+
+    # 1.) The widget for selcting the statistics to be used and displaying the histogram.
+    stat_widget = _create_stat_widget(statistics, default_stat)
+
+    # 2.) The widget for setting the threshold and updating the positive / negative classification based on it.
+    stat_names = stat_widget.stat_names
+    step = 1
+    all_values = statistics[stat_names].values
+    min_val = all_values.min()
+    max_val = all_values.max()
+
+    @magicgui(
+        threshold={
+            "widget_type": "FloatSlider",
+            "label": "Threshold",
+            "min": min_val,
+            "max": max_val,
+            "step": step,
+        },
+        call_button="Apply",
+    )
+    def threshold_widget(viewer: napari.Viewer, threshold: float = (max_val - min_val) / 2):
+        label_ids = statistics.label_id.values
+        stat_name = stat_widget.param_box.currentText()
+        vals = statistics[stat_name].values
+        pos_ids = label_ids[vals >= threshold]
+        neg_ids = label_ids[vals <= threshold]
+        data_labels = np.zeros(gfp.shape, dtype="uint8")
+        data_labels[np.isin(sgns_extended, pos_ids)] = 2
+        data_labels[np.isin(sgns_extended, neg_ids)] = 1
+        viewer.layers["positive-negative"].data = data_labels
+
+    threshold_widget.viewer.value = v
+
+    # Bind the widgets.
+    v.window.add_dock_widget(stat_widget, area="right")
+    v.window.add_dock_widget(threshold_widget, area="right")
+    stat_widget.setWindowTitle("GFP Histogram")
+
+    napari.run()
+
+
+# Cochlea chanel registration quality:
+# - M_LR_000144_L: rough alignment is ok, but specific alignment is a bit poor.
+# - M_LR_000145_L: rough alignment is ok, detailed alignment also ok.
+# - M_LR_000151_R: rough alignment is ok, detailed alignment also ok.
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("prefix")
+    args = parser.parse_args()
+
+    gfp_annotation(args.prefix)
+
+
+if __name__ == "__main__":
+    main()