Added cell stat + example notebook

Author: C-Achard

.gitignore

@@ -7,7 +7,8 @@ __pycache__/
 *.so
 
 # unwanted results files
-*.csv
+*.tif
+napari_cellseg3d/_tests/res/*.csv
 
 # Distribution / packaging
 .Python

docs/res/code/utils.rst

@@ -33,6 +33,14 @@ dice_coeff
 **************************************
 .. autofunction:: napari_cellseg3d.utils::dice_coeff
 
+sphericity_volume_area
+**************************************
+.. autofunction:: napari_cellseg3d.utils::sphericity_volume_area
+
+sphericity_axis
+**************************************
+.. autofunction:: napari_cellseg3d.utils::sphericity_axis
+
 normalize_x
 **************************************
 .. autofunction:: napari_cellseg3d.utils::normalize_x

docs/res/guides/inference_module_guide.rst

@@ -41,6 +41,9 @@ Interface and functionalities
   | You can then choose one of the provided **models** above, which will be used for inference.
   | You may also choose to **load custom weights** rather than the pre-trained ones, simply ensure they are **compatible** (e.g. produced from the training module for the same model)
 
+.. note::
+    Currently the SegResNet model requires you to provide the size of the images the model was trained with due to the VAE module.
+    Provided weights use a size of 128, please leave it as is if you're not using custom weights.
 
 * **Inference parameters** :
 

napari_cellseg3d/model_framework.py

@@ -230,6 +230,7 @@ def display_status_report(self):
         self.progress.setValue(0)
 
     def toggle_weights_path(self):
+        """Toggle visibility of weight path"""
         self.toggle_visibility(
             self.custom_weights_choice, self.weights_path_container
         )

napari_cellseg3d/model_instance_seg.py

@@ -3,11 +3,18 @@
 
 import numpy as np
 from skimage.measure import label
+from skimage.measure import marching_cubes
+from skimage.measure import mesh_surface_area
+from skimage.measure import regionprops
 from skimage.morphology import remove_small_objects
 from skimage.segmentation import watershed
 from skimage.transform import resize
 from tifffile import imread
 
+from napari_cellseg3d.utils import fill_list_in_between
+from napari_cellseg3d.utils import sphericity_axis
+from napari_cellseg3d.utils import sphericity_volume_area
+
 
 def binary_connected(
     volume, thres=0.5, thres_small=3, scale_factors=(1.0, 1.0, 1.0)
@@ -162,3 +169,43 @@ def to_semantic(image, is_file_path=False):
     image[image >= 1] = 1
     result = image.astype(np.uint16)
     return result
+
+
+def volume_stats(volume_image):
+
+    properties = regionprops(volume_image)
+    number_objects = np.amax(volume_image)
+
+    sphericity_va = []
+    sphericity_ax = [
+        sphericity_axis(
+            region.axis_major_length * 0.5, region.axis_minor_length * 0.5
+        )
+        for region in properties
+    ]
+    # for region in properties:
+    # object = (volume_image == region.label).transpose(1, 2, 0)
+    # verts, faces, _, values = marching_cubes(
+    #     object, level=0, spacing=(1.0, 1.0, 1.0)
+    # )
+    # surface_area_pixels = mesh_surface_area(verts, faces)
+    # sphericity_va.append(
+    #     sphericity_volume_area(region.area, surface_area_pixels)
+    # )
+
+    volume = [region.area for region in properties]
+
+    def fill(lst, n=len(properties) - 1):
+        return fill_list_in_between(lst, n, "")
+
+    return {
+        "Volume": volume,
+        "Centroid": [region.centroid for region in properties],
+        # "Sphericity (volume/area)": sphericity_va,
+        "Sphericity (axes)": sphericity_ax,
+        "Image size": fill([volume_image.shape]),
+        "Total image volume": fill([len(volume_image.flatten())]),
+        "Total object volume (pixels)": fill([np.sum(volume)]),
+        "Filling ratio": fill([np.sum(volume) / len(volume_image.flatten())]),
+        "Number objects": fill([len(properties)]),
+    }

napari_cellseg3d/model_workers.py

@@ -181,9 +181,7 @@ def log_parameters(self):
             self.log(f"Dataset loaded on {self.device}")
 
         if self.transforms["zoom"][0]:
-            self.log(
-                f"Anisotropy parameters are : {self.transforms['zoom'][1]} microns in x,y,z"
-            )
+            self.log(f"Scaling factor : {self.transforms['zoom'][1]} (x,y,z)")
 
         if self.instance_params["do_instance"]:
             self.log(
@@ -436,7 +434,7 @@ def method(image):
                     instance_filepath = (
                         self.results_path
                         + "/"
-                        + f"Instance_seg_labels_{image_id}"
+                        + f"Instance_seg_labels_{image_id}_"
                         + original_filename
                         + "_"
                         + self.model_dict["name"]

napari_cellseg3d/plugin_helper.py

@@ -28,6 +28,7 @@ def __init__(self, viewer: "napari.viewer.Viewer"):
         self.btn2 = ui.make_button(
             "About...", lambda: ui.open_url(self.about_url)
         )
+
         self.btnc = ui.make_button("Close", self.remove_from_viewer)
 
         self.build()

napari_cellseg3d/plugin_model_inference.py

@@ -3,6 +3,7 @@
 
 import napari
 import numpy as np
+import pandas as pd
 
 # Qt
 from qtpy.QtWidgets import QSizePolicy
@@ -11,6 +12,7 @@
 from napari_cellseg3d import interface as ui
 from napari_cellseg3d import utils
 from napari_cellseg3d.model_framework import ModelFramework
+from napari_cellseg3d.model_instance_seg import volume_stats
 from napari_cellseg3d.model_workers import InferenceWorker
 
 
@@ -70,6 +72,7 @@ def __init__(self, viewer: "napari.viewer.Viewer"):
         self.zoom = [1, 1, 1]
 
         self.instance_params = None
+        self.stats_to_csv = False
 
         self.keep_on_cpu = False
         self.use_window_inference = False
@@ -211,6 +214,9 @@ def __init__(self, viewer: "napari.viewer.Viewer"):
             left_or_above=self.instance_small_object_thresh_lbl,
             horizontal=False,
         )
+        self.save_stats_to_csv_box = ui.make_checkbox(
+            "Save stats to csv", parent=self
+        )
 
         (
             self.instance_param_container,
@@ -315,6 +321,7 @@ def build(self):
                 self.instance_method_choice,
                 self.instance_prob_t_container,
                 self.instance_small_object_t_container,
+                self.save_stats_to_csv_box,
             ],
         )
 
@@ -557,6 +564,7 @@ def start(self):  # TODO update
                 "threshold": self.instance_prob_thresh.value(),
                 "size_small": self.instance_small_object_thresh.value(),
             }
+            self.stats_to_csv = self.save_stats_to_csv_box.isChecked()
             # print(f"METHOD : {self.instance_method_choice.currentText()}")
 
             self.show_res_nbr = self.display_number_choice.value()
@@ -687,15 +695,35 @@ def on_yield(data, widget):
 
             if data["instance_labels"] is not None:
 
-                number_cells = np.amax(data["instance_labels"])
-
-                widget.log.print_and_log(
-                    f"\nNUMBER OF CELLS : {number_cells}\n"
-                )
-
+                labels = data["instance_labels"]
                 method = widget.instance_params["method"]
+                number_cells = np.amax(labels)
+
                 name = f"({number_cells})_{method}_instance_labels_{image_id}"
 
-                instance_layer = viewer.add_labels(
-                    data[f"instance_labels"], name=name
-                )
+                instance_layer = viewer.add_labels(labels, name=name)
+
+                if widget.stats_to_csv:  # TODO move to worker
+
+                    cell_data = volume_stats(
+                        labels
+                    )  # TODO test with area mesh function
+                    # count = np.tile("", len(cell_data["Volume"])-1)
+                    # cell_data["Cell count"] = np.insert(count, 0, number_cells)
+                    # cell_data["Total cell volume"] = np.insert(
+                    #     count, 0, tot_cell_volume
+                    # )
+                    # cell_data["Cell volume ratio"] = np.insert(
+                    #     count, 0, tot_cell_volume / len(labels.flatten())
+                    # )
+
+                    numeric_data = pd.DataFrame(cell_data)
+
+                    csv_name = f"/{method}_seg_results_{image_id}_{utils.get_date_time()}.csv"
+                    numeric_data.to_csv(
+                        widget.results_path + csv_name, index=False
+                    )
+
+                    # widget.log.print_and_log(
+                    #     f"\nNUMBER OF CELLS : {number_cells}\n"
+                    # )

napari_cellseg3d/plugin_model_training.py

@@ -985,7 +985,7 @@ def make_csv(self):
             }
         )
         path = os.path.join(self.results_path_folder, "training.csv")
-        self.df.to_csv(path)
+        self.df.to_csv(path, index=False)
 
     def plot_loss(self, loss, dice_metric):
         """Creates two subplots to plot the training loss and validation metric"""

napari_cellseg3d/utils.py

@@ -60,6 +60,35 @@ def normalize_y(image):
     return image
 
 
+def sphericity_volume_area(volume, surface_area):
+    """Computes the sphericity from volume and area
+
+    .. math::
+       sphericity =\\frac {\\pi^\\frac{1}{3} (6 V_{p})^\\frac{2}{3}} {A_p}
+
+    """
+    return np.pi ** (1 / 3) * (6 * volume) ** (2 / 3) / surface_area
+
+
+def sphericity_axis(semi_major, semi_minor):
+    """Computes the sphericity from volume semi major (a) and semi minor (b) axes.
+
+    .. math::
+        sphericity = \\frac {2 \\sqrt[3]{ab^2}} {a+ \\frac {b^2} {\\sqrt{a^2-b^2}}ln( \\frac {a+ \\sqrt{a^2-b^2}} {b} )}
+
+    """
+    a = semi_major
+    b = semi_minor
+
+    root = (a**2 - b**2) ** (1 / 2)
+
+    return (
+        2
+        * (a * (b**2)) ** (1 / 3)
+        / (a + (b**2) / root * np.log((a + root) / b))
+    )
+
+
 def dice_coeff(y_true, y_pred):
     """Compute Dice-Sorensen coefficient between two numpy arrays