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Dimensions: (91, 71, 584, 600, 1) BTYXC + - Tracks: 11,039 + - Divisions: 1,608 +- `val.trk` + - Dimensions: (27, 71, 584, 600, 1) BTYXC + - Tracks: 2,923 + - Divisions: 444 +- `test.trk` + - Dimensions: (12, 71, 584, 600, 1) BTYXC + - Tracks: 1,213 + - Divisions: 178 +- `data-source.npz` + - A record of the source of each batch divided into one array per data split + +Each `trks` file contains three components: +- X: raw fluorescent nuclear data +- y: nuclear segmentation masks +- lineages: lineage records including the cell id, frames present and division links from parents to daughter cells + +## Change Log +DynamicNuclearNet 1.0 (June 2023): The original dataset used for all experiments in Schwartz et al. 2023 + +## Instructions for use + +```python +import os + +import numpy as np +import pandas as pd + +from deepcell_tracking.trk_io import load_trks + +data_dir = 'path_to_data' +data = load_trks(os.path.join(data_dir, 'test.trks')) + +X = data['X'] +y = data['y'] +lineages = data['lineages'] + +data_source = np.load(os.path.join(data_dir, 'data-source.npz'), allow_pickle=True) +meta = pd.DataFrame(data_source['test'], columns=['filename', 'experiment', 'pixel_size', 'screening_passed', 'time_step', 'specimen']) +``` diff --git a/DEEPCELL/convert_trk_to_CTC.py b/DEEPCELL/convert_trk_to_CTC.py new file mode 100644 index 0000000000000000000000000000000000000000..12ba64fe9aaad537e433462f6b636fdae73cb417 --- /dev/null +++ b/DEEPCELL/convert_trk_to_CTC.py @@ -0,0 +1,250 @@ +import os +import numpy as np +import pandas as pd +from deepcell_tracking.trk_io import load_trks +from skimage.io import imsave +import tifffile + +def convert_to_ctc_format(data_dir, output_dir): + """ + Convert DynamicNuclearNet tracking data to CTC format + + CTC format requires: + - SEG/ folder with segmentation masks (man_seg####.tif) + - TRA/ folder with tracking masks (man_track####.tif) + - res_track.txt file with tracking results + """ + + # Load the tracking data + data = load_trks(os.path.join(data_dir, 'test.trks')) + + X = data['X'] # Image data + y = data['y'] # Segmentation masks + lineages = data['lineages'] # Tracking information + + # Load metadata + data_source = np.load(os.path.join(data_dir, 'data-source.npz'), allow_pickle=True) + meta = pd.DataFrame(data_source['test'], columns=['filename', 'experiment', 'pixel_size', 'screening_passed', 'time_step', 'specimen']) + + # Create output directories + seg_dir = os.path.join(output_dir, 'SEG') + tra_dir = os.path.join(output_dir, 'TRA') + os.makedirs(seg_dir, exist_ok=True) + os.makedirs(tra_dir, exist_ok=True) + + # Process each sequence + tracking_results = [] + + for seq_idx in range(len(X)): + print(f"Processing sequence {seq_idx + 1}/{len(X)}") + + sequence_images = X[seq_idx] # Shape: (T, H, W, C) + sequence_masks = y[seq_idx] # Shape: (T, H, W, 1) + sequence_lineages = lineages[seq_idx] + + # Remove channel dimension from masks if present + if sequence_masks.ndim == 4: + sequence_masks = sequence_masks.squeeze(-1) + + num_frames = sequence_masks.shape[0] + + # Create tracking masks and collect lineage information + for t in range(num_frames): + # Save segmentation mask + seg_filename = f"man_seg{t:04d}.tif" + seg_path = os.path.join(seg_dir, seg_filename) + tifffile.imwrite(seg_path, sequence_masks[t].astype(np.uint16)) + + # Create tracking mask (same as segmentation for this format) + tra_filename = f"man_track{t:04d}.tif" + tra_path = os.path.join(tra_dir, tra_filename) + tifffile.imwrite(tra_path, sequence_masks[t].astype(np.uint16)) + + # Extract tracking information for this frame + frame_mask = sequence_masks[t] + unique_cells = np.unique(frame_mask) + unique_cells = unique_cells[unique_cells > 0] # Remove background + + for cell_id in unique_cells: + # Find lineage information for this cell + cell_lineage = None + for lineage in sequence_lineages: + if cell_id in lineage.get('label', []): + cell_lineage = lineage + break + + if cell_lineage: + parent_id = cell_lineage.get('parent', 0) + generation = cell_lineage.get('generation', 0) + + # CTC format: [cell_id, start_frame, end_frame, parent_id] + # We need to determine start and end frames for each cell + cell_frames = [] + for frame_idx in range(num_frames): + if cell_id in np.unique(sequence_masks[frame_idx]): + cell_frames.append(frame_idx) + + if cell_frames: + start_frame = min(cell_frames) + end_frame = max(cell_frames) + + tracking_results.append([ + cell_id, + start_frame, + end_frame, + parent_id + ]) + + # Save tracking results in CTC format + tracking_df = pd.DataFrame(tracking_results, columns=['L', 'B', 'E', 'P']) + tracking_df = tracking_df.drop_duplicates().sort_values('L') + + # Write res_track.txt file + res_track_path = os.path.join(output_dir, 'res_track.txt') + with open(res_track_path, 'w') as f: + for _, row in tracking_df.iterrows(): + f.write(f"{int(row['L'])} {int(row['B'])} {int(row['E'])} {int(row['P'])}\n") + + print(f"CTC format conversion completed!") + print(f"Segmentation masks saved to: {seg_dir}") + print(f"Tracking masks saved to: {tra_dir}") + print(f"Tracking results saved to: {res_track_path}") + + return tracking_df + +# Alternative function for batch processing all .trks files +def convert_all_trks_to_ctc(data_dir, output_base_dir): + """ + Convert all .trks files (train, test, val) to CTC format + """ + trks_files = ['train.trks', 'test.trks', 'val.trks'] + + for trks_file in trks_files: + if os.path.exists(os.path.join(data_dir, trks_file)): + dataset_name = trks_file.split('.')[0] + output_dir = os.path.join(output_base_dir, dataset_name) + + print(f"\nConverting {trks_file} to CTC format...") + + # Load data + data = load_trks(os.path.join(data_dir, trks_file)) + + # Create output directory + os.makedirs(output_dir, exist_ok=True) + + # Convert each sequence in the dataset + for seq_idx in range(len(data['X'])): + seq_output_dir = os.path.join(output_dir, f"sequence_{seq_idx:03d}") + + # Create single sequence data + single_seq_data = { + 'X': [data['X'][seq_idx]], + 'y': [data['y'][seq_idx]], + 'lineages': [data['lineages'][seq_idx]] + } + + # Convert this sequence + convert_single_sequence_to_ctc(single_seq_data, seq_output_dir) + +def trk_to_isbi(track, path=None): + """Convert a lineage track into an ISBI formatted text file. + + Args: + track (dict): Cell lineage object. + path (str): Path to save the .txt file. + + Returns: + pd.DataFrame: DataFrame of ISBI data for each label. + """ + isbi = [] + for label in track: + first_frame = min(track[label]['frames']) + last_frame = max(track[label]['frames']) + parent = track[label]['parent'] + parent = 0 if parent is None else parent + if parent: + parent_frames = track[parent]['frames'] + if parent_frames[-1] != first_frame - 1: + parent = 0 + + isbi_dict = {'Cell_ID': label, + 'Start': first_frame, + 'End': last_frame, + 'Parent_ID': parent} + isbi.append(isbi_dict) + + if path is not None: + with open(path, 'w') as text_file: + for cell in isbi: # Fixed: iterate over isbi list, not isbi_dict + line = '{cell_id} {start} {end} {parent}\n'.format( + cell_id=cell['Cell_ID'], + start=cell['Start'], + end=cell['End'], + parent=cell['Parent_ID'] + ) + text_file.write(line) + df = pd.DataFrame(isbi) + return df + +def convert_single_sequence_to_ctc(data, output_dir): + """ + Convert a single sequence to CTC format + """ + X = data['X'][0] # Single sequence + y = data['y'][0] # Single sequence masks + lineages = data['lineages'][0] # Single sequence lineages + + # Debug: Print lineage structure + print(f"Lineages type: {type(lineages)}") + print(f"Lineages shape/length: {len(lineages) if hasattr(lineages, '__len__') else 'N/A'}") + if len(lineages) > 0: + if isinstance(lineages, dict): + first_key = list(lineages.keys())[0] + print(f"First lineage key: {first_key}") + print(f"First lineage value type: {type(lineages[first_key])}") + print(f"First lineage value: {lineages[first_key]}") + print(f"All keys (first 10): {list(lineages.keys())[:10]}") + else: + print(f"First lineage item type: {type(lineages[0])}") + print(f"First lineage item: {lineages[0]}") + + # Create output directories + seg_dir = os.path.join(output_dir, 'SEG') + tra_dir = os.path.join(output_dir, 'TRA') + os.makedirs(seg_dir, exist_ok=True) + os.makedirs(tra_dir, exist_ok=True) + + # Remove channel dimension if present + if y.ndim == 4: + y = y.squeeze(-1) + + # Process each frame - save segmentation and tracking masks + for t in range(y.shape[0]): + # Save segmentation mask + seg_filename = f"man_seg{t:04d}.tif" + tifffile.imwrite(os.path.join(seg_dir, seg_filename), y[t].astype(np.uint16)) + + # Save tracking mask (same as segmentation) + tra_filename = f"man_track{t:04d}.tif" + tifffile.imwrite(os.path.join(tra_dir, tra_filename), y[t].astype(np.uint16)) + + # Convert lineages to CTC format using the trk_to_isbi function + res_track_path = os.path.join(output_dir, 'res_track.txt') + + # Use the trk_to_isbi function to convert lineages + isbi_df = trk_to_isbi(lineages, res_track_path) + + print(f"Converted {len(isbi_df)} cell tracks to CTC format") + return isbi_df + + +# Usage example: +if __name__ == "__main__": + data_dir = '/l/users/sahal.mullappilly/Komal/documents/Cell/DEEPCELL/DynamicNuclearNet-tracking-v1_0' # Path to your DynamicNuclearNet data + output_dir = '/l/users/sahal.mullappilly/Komal/documents/Cell/DEEPCELL/CTCformat' # Where to save CTC format + + # Convert all datasets + convert_all_trks_to_ctc(data_dir, output_dir) + + # Or convert just the test set + # convert_to_ctc_format(data_dir, os.path.join(output_dir, 'test')) \ No newline at end of file