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Table of Contents: -- [Emission plot Fig. 1c](#emission-plot-fig.-1c) +- [Emission plot Fig. 1c](#emission-plot-fig-1c) - [DAPI nuclei cell segmentation in - 2D.](#dapi-nuclei-cell-segmentation-in-2d.) + 2D.](#dapi-nuclei-cell-segmentation-in-2d) - [Installation](#installation) - [Segmentation training](#segmentation-training) - [Prediction](#prediction) @@ -124,6 +124,12 @@ axis(1, at=seq(300,800,by=50)) ![](figure01_files/figure-commonmark/unnamed-chunk-2-1.png) +``` r +peak.max +``` + + [1] 517 515 613 613 + Save the plot. ``` r @@ -158,6 +164,42 @@ round(AZdye594, 2) [1] 2.88 +``` r +print(488) +``` + + [1] 488 + +``` r +quench.ratio$`max(emission)`[1] +``` + + [1] 0.9991601 + +``` r +quench.ratio$`max(emission)`[2] +``` + + [1] 0.174122 + +``` r +print(594) +``` + + [1] 594 + +``` r +quench.ratio$`max(emission)`[3] +``` + + [1] 0.9991601 + +``` r +quench.ratio$`max(emission)`[4] +``` + + [1] 0.3466485 + ## DAPI nuclei cell segmentation in 2D. ### Installation @@ -213,36 +255,18 @@ Open up tensorboard to follow the results: tensorboard --logdir=. ``` -![Tensorboard lets you monitor the training of the neural -network](./repo_img/tensorboard.png) +
+ + +
Click the **images** tab of the Tensorboard to inspect the visual output of the training. -
- - --- - - - - - -
-

begin later

-
- -Figure 1: Training monitoring. Left is in the beginning right is later -during training. Top row is input, middle is output of network and -bottom is the reference (what the network should aim for). - -
+begin In detail: @@ -260,31 +284,6 @@ We have a script we can apply to any image for prediction. python predict_nuclei.py ``` -
- - ---- - - - - - - -
-

-
-

-
- -Figure 2: Segmentation results. - -
+ + + diff --git a/figure01.py b/figure01.py new file mode 100644 index 0000000..2cb9816 --- /dev/null +++ b/figure01.py @@ -0,0 +1,217 @@ +from __future__ import print_function, unicode_literals, absolute_import, division +import argparse + +import sys +import numpy as np +import matplotlib +matplotlib.rcParams["image.interpolation"] = 'none' +import matplotlib.pyplot as plt + +from glob import glob +from tifffile import imread +from csbdeep.utils import Path, normalize +from csbdeep.io import save_tiff_imagej_compatible + +from stardist import random_label_cmap, _draw_polygons, export_imagej_rois +from stardist.models import StarDist2D + +#Load previously saved model +from keras.models import load_model +from tensorflow.keras.metrics import MeanIoU + +import tensorflow as tf +import datetime + +from tensorflow.keras.utils import normalize +import os +import cv2 +from PIL import Image +import numpy as np +from matplotlib import pyplot as plt +from sklearn.preprocessing import MinMaxScaler +from keras.optimizers import Adam + +from glob import glob +from tqdm import tqdm +from tifffile import imread +from csbdeep.utils import Path, normalize + +import pandas as pd + +def calculate_padded_dimensions(original_height, original_width, tile_size=256): + padded_height = (original_height + tile_size - 1) // tile_size * tile_size + padded_width = (original_width + tile_size - 1) // tile_size * tile_size + return padded_height, padded_width + +#path = '/Users/danielfurth/Documents/GitHub/FluorPLA/figure01/230913/MAX_CMYC_MAX_1_MMStack_Pos0.ome.tif' + +def main(image_path): + model = load_model("cytosol_unet_400epochs.hdf5", compile=False) + + # Get the basename of the image file + image_basename = os.path.basename(image_path) + + # Remove the file extension and add '.csv' as the new extension + csv_file = os.path.splitext(image_basename)[0] + '.csv' + + # List to store the loaded image + multiP = [] + + ret, multiP = cv2.imreadmulti(image_path, flags=cv2.IMREAD_ANYDEPTH) + + # Check if images were loaded successfully + if ret: + # Separate the channels + antibody = multiP[0] # multiP[2] # First channel + nuclei = multiP[1] # Second channel + cytosol = multiP[2] # multiP[0] # Third channel + + # Create a mask for values below the threshold + maskTmp = (antibody < 150) # 400 + + # Set values below the threshold to 0 + antibody[maskTmp] = 0 + + + image_dataset = np.array(cytosol) + + image_dataset = image_dataset /image_dataset.max() #Can also normalize or scale using MinMax scaler + + + # Assuming you have a NumPy array called 'image_dataset' with shape (2808, 2688) + original_height, original_width = image_dataset.shape + tile_size = 256 + + padded_height, padded_width = calculate_padded_dimensions(original_height, original_width) + image_dataset_padded = np.pad(image_dataset, ((0, padded_height - original_height), (0, padded_width - original_width)), mode='constant') + + # Calculate the number of tiles in both dimensions + num_tiles_height = padded_height // tile_size + num_tiles_width = padded_width // tile_size + + # Initialize the tiled image array + tiledImage = np.zeros((num_tiles_height * num_tiles_width, tile_size, tile_size, 1), dtype=np.float32) + + # Iterate over the original image and extract tiles + tile_index = 0 + for i in range(num_tiles_height): + for j in range(num_tiles_width): + # Extract a 256x256 tile from the original image + tile = image_dataset_padded[i * tile_size: (i + 1) * tile_size, j * tile_size: (j + 1) * tile_size] + tile = np.expand_dims(tile, axis=-1) # Add a channel dimension + tiledImage[tile_index] = tile + tile_index += 1 + + + y_pred=model.predict(tiledImage) + + # Assuming you have a NumPy array called 'tiledImage' with shape (x, 256, 256, 1) + num_tiles, tile_height, tile_width, num_channels = y_pred.shape + tile_size = 256 + + # Calculate the dimensions of the original image + new_height = num_tiles_height * tile_height + new_width = num_tiles_width * tile_width + + # Initialize the original image array + reconstructed_image = np.zeros((new_height, new_width, num_channels), dtype=np.float32) + + # Iterate over the tiles and reconstruct the original image + tile_index = 0 + for i in range(num_tiles_height): + for j in range(num_tiles_width): + # Get the tile from 'tiledImage' + tile = y_pred[tile_index] + + # Place the tile in the reconstructed image + reconstructed_image[i * tile_size: (i + 1) * tile_size, j * tile_size: (j + 1) * tile_size] = tile + tile_index += 1 + + + float32_array = reconstructed_image + + # Assuming you have a NumPy array called 'float32_array' + # Scale the float32 array to the range [0, 255] + scaled_array = ((float32_array - float32_array.min()) / (float32_array.max() - float32_array.min()) * 255).astype(np.uint8) + + # Create a monochrome OpenCV Mat + monochrome_mat = cv2.cvtColor(scaled_array, cv2.COLOR_GRAY2BGR) + + # Convert the BGR image to grayscale + monochrome_mat = cv2.cvtColor(monochrome_mat, cv2.COLOR_BGR2GRAY) + + monochrome_mat = monochrome_mat[:original_height, :original_width] + + # Apply a Gaussian filter with a 3-pixel sigma + sigma = 3 + filtered_image = cv2.GaussianBlur(monochrome_mat, (0, 0), sigma) + + # Threshold the filtered image to create a binary image + _, binary_image = cv2.threshold(filtered_image, 0, 255, cv2.THRESH_BINARY) + + # Perform image segmentation to create a labeled image + num_labels, labeled_image = cv2.connectedComponents(binary_image) + + # Note: 'labeled_image' now contains the labeled regions. Each region has a unique integer label. + # 'num_labels' represents the total number of labeled regions. + + # You can display the labeled image using a colormap for visualization: + #colored_labeled_image = cv2.applyColorMap((labeled_image * 255 / num_labels).astype(np.uint8), cv2.COLORMAP_JET) + + + axis_norm = (0,1) # normalize channels independently + # axis_norm = (0,1,2) # normalize channels jointly + + model = StarDist2D(None, name='dapi', basedir='models') + + img = normalize(nuclei, 1,99.8, axis=axis_norm) + labels, details = model.predict_instances(img) + + # Get unique labels in the "labels" image + unique_labels = np.unique(labels) + + # Create a DataFrame to store label intensities + data = {"Label": [], "Parent": [], "Cyto": [], "Background": [], "Nuclei": []} + + # Iterate through unique labels and calculate mean intensity + for label in unique_labels: + if label == 0: + continue # Skip background label (0) + + # Create a mask for the current label + label_mask = (labels == label).astype(np.int8) + + + # Calculate the mean intensity within the mask in the "antibody" image + label_intensity = np.mean(antibody[label_mask == 1]) + parent = int(np.mean(labeled_image[label_mask == 1])) + + cytomask = (labeled_image == parent).astype(np.int8) + label_mask2 = cytomask - label_mask + background = np.mean(antibody[labeled_image == 0]) + + cyto_intensity = np.mean(antibody[label_mask2 == 1]) + + # Append the data to the DataFrame + data["Label"].append(label) + data["Parent"].append(parent) + data["Background"].append(background) + data["Cyto"].append(cyto_intensity) + data["Nuclei"].append(label_intensity) + + + # Create a DataFrame + df = pd.DataFrame(data) + + # Save the results to a CSV file + #csv_file = "nuclei_intensities.csv" + df.to_csv(csv_file, index=False) + + print(f"Results saved to {csv_file}") + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Image processing script") + parser.add_argument("image_path", type=str, help="Path to the image file") + + args = parser.parse_args() + main(args.image_path)