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5 results for “3D-reconstruction”
Fig. 1 in 3D-reconstruction of the female of fossil Revelieria groehni Sergi, Perkovsky et Reike, 2013 (Coleoptera: Latridiidae) from Eocene Baltic amber
Fig. 1. Photomicrographs of Revelieria groehni, female, Baltic amber, JDC9116 [JDC], habitus: A – dorsolateral view; B – ventrolateral view.
Fig. 3. X in 3D-reconstruction of the female of fossil Revelieria groehni Sergi, Perkovsky et Reike, 2013 (Coleoptera: Latridiidae) from Eocene Baltic amber
Fig. 3. Xray mCT renderings of Revelieria groehni, female, Baltic amber, JDC9116 [JDC], details of forebody: A – dorsal view; B – ventral view.
Fig. 2. X in 3D-reconstruction of the female of fossil Revelieria groehni Sergi, Perkovsky et Reike, 2013 (Coleoptera: Latridiidae) from Eocene Baltic amber
Fig. 2. Xray mCT renderings of Revelieria groehni, female, Baltic amber, JDC9116 [JDC], habitus: A – dorsal view; B – ventral view; C – left lateral view.
Supplemental material for 'Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection'
<p>This is the supplemental data for the manuscript titled ‘<em>Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection’</em> submitted to <em>Ultramicroscopy</em>.</p> <p><strong>Motivation:</strong></p> <p>Detection of nanoparticles and classification of the material type in scanning transmission electron microscopy (STEM) images can be a tedious task, if it has to be done manually. Therefore, a convolutional neural network (CNN) is trained to do this task for STEM-images of TiO<sub>2</sub>-WO<sub>3</sub> nanoparticle hetero-aggregates. In conventional STEM, only 2D-projection images of the samples can be measured. STEM tomography allows for a 3D-reconstruction but it is a time-consuming and hence expensive task. In the present work, evaluations of 2D-projections are compared quantitatively to evaluations of 3D-reconstructions. For both evaluations a CNN is trained to predict particle positions and classify the material. The present dataset contains training and evaluation data and some code scripts that can be used after installation of the MMDetection toolbox (<a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a>) to train the CNN for 2D-projection data. For 3D-reconstruction a StarDist-3D network is trained (<a href="https://github.com/stardist/stardist">https://github.com/stardist/stardist</a>). Details are provided in the manuscript submitted to Ultramicroscopy and in the comments of the code scripts. For evaluation, we provide Python and MATLAB scripts.</p> <p><strong>Authors and funding:</strong></p> <p>The present dataset was created by the authors. The work was funded by the Deutsche Forschungsgemeinschaft within the priority program SPP2289 under contract numbers RO2057/17-1 and MA3333/25-1 and under contract number INST 144/462-1 FUGG.</p> <pre> </pre> <p><strong>Dataset description:</strong></p> <p>We provide several zip-archives. All of them contain two subfolders, one of them for 2D-projection data, the other one for 3D-reconstruction data.</p> <p><em>training_data.zip</em> contains the training data. In the 3D case, the subfolder <em>mask</em> contains the ground truth segmentation masks; the subfolder <em>reconstruction</em> contains the corresponding simulated 3D reconstructions. In the 2D case, the subfolder <em>HAADF</em> contains the 2D-projection images. In both cases, the subfolder <em>json </em>contains the annotation. Each file within the <em>json</em> folder provides for each image or reconstruction the following information:</p> <p>· aggregat_no: image id, the number of the corresponding image file</p> <p>· particle_position_x: list of particle position x-coordinates in nm</p> <p>· particle_position_y: list of particle position y-coordinates in nm</p> <p>· particle_position_z: list of particle position z-coordinates in nm</p> <p>· particle_radius: list of volume equivalent particle radii in nm</p> <p>· particle_type: list of particle types, 1: TiO<sub>2</sub>, 2: WO<sub>3</sub></p> <p>· particle_shape: list of particle shapes: 0: sphere, 1: box, 2: icosahedron</p> <p>· rotation: list of particle rotations in rad. Each particle is rotated twice by the listed angle (before and after deformation)</p> <p>· deformation: list of particle deformations. After the first rotation the particle x-coordinates of the particle’s surface mesh are scaled by the factor listed in deformation, y- and z-coordinates are scaled according to 1/sqrt(deformation).</p> <p>· cluster_index: list of cluster indices for each particle</p> <p>· initial_cluster_index: list of initial cluster indices for each particle, before primary clusters of the same material were merged</p> <p>· fractal_dimension: the intended fractal dimension of the aggregate</p> <p>· fractal_dimension_true: the realized geometric fractal dimension of the aggregate (neglecting particle densities)</p> <p>· fractal_dimension_weight_true: the realized fractal dimension of the aggregate (including particle densities)</p> <p>· fractal_prefactor: fractal prefactor</p> <p>· mixing_ratio_intended: the intended mixing ratio (fraction of WO<sub>3</sub> particles)</p> <p>· mixing_ratio_true: the realised mixing ratio (fraction of WO<sub>3</sub> particles)</p> <p>· mixing_ratio_volume: the realised mixing ratio (fraction of WO<sub>3</sub> volume)</p> <p>· mixing_ratio_weight: the realised mixing ratio (fraction of WO<sub>3</sub> weight)</p> <p>· particle_1_rho: density of TiO<sub>2</sub> used for the calculations</p> <p>· particle_1_size_mean: mean TiO<sub>2</sub> radius</p> <p>· particle_1_size_min: smallest TiO<sub>2</sub> radius</p> <p>· particle_1_size_max: largest TiO<sub>2</sub> radius</p> <p>· particle_1_size_std: standard deviation of TiO<sub>2</sub> radii</p> <p>· particle_1_clustersize: average TiO<sub>2</sub> cluster size</p> <p>· particle_1_clustersize_init: average TiO<sub>2</sub> cluster size of primary clusters (before merging into larger clusters)</p> <p>· particle_1_clustersize_init_intended: intended TiO<sub>2</sub> cluster size of primary clusters</p> <p>· particle_2_rho: density of WO<sub>3 </sub>used for the calculations</p> <p>· particle_2_size_mean: mean WO<sub>3</sub> radius</p> <p>· particle_2_size_min: smallest WO<sub>3</sub> radius</p> <p>· particle_2_size_max: largest WO<sub>3</sub> radius</p> <p>· particle_2_size_std: standard deviation of WO<sub>3</sub> radii</p> <p>· particle_2_clustersize: average WO<sub>3</sub> cluster size</p> <p>· particle_2_clustersize_init: average WO<sub>3</sub> cluster size of primary clusters (before merging into larger clusters)</p> <p>· particle_2_clustersize_init_intended: intended WO<sub>3</sub> cluster size of primary clusters</p> <p>· number_of_primary_particles: number of particles within the aggregate</p> <p>· gyration_radius_geometric: gyration radius of the aggregate (neglecting particle densities)</p> <p>· gyration_radius_weighted: gyration radius of the aggregate (including particle densities)</p> <p>· mean_coordination: mean total coordination number (particle contacts)</p> <p>· mean_coordination_heterogen: mean heterogeneous coordination number (contacts with particles of the different material)</p> <p>· mean_coordination_homogen: mean homogeneous coordination number (contacts with particles of the same material)</p> <p>· material_1: the name of the first material (TiO2)</p> <p>· material_2: the name of the second material (WO3)</p> <p>· radius_equiv: list of area equivalent particle radii (in projection) in nm</p> <p>· k_proj: projection direction of the aggregate: 0: z-direction (axis = 2), 1: x-direction (axis = 1), 2: y-direction (axis = 0)</p> <p>· polygons: list of polygons that surround the particle (COCO annotation)</p> <p>· bboxes: list of particle bounding boxes</p> <p>· aggregate_size: projected area of the aggregate translated into the radius of a circle in nm</p> <p>· n_pix: number of pixel per image in horizontal and vertical direction (squared images)</p> <p>· pixel_size: pixel size in nm</p> <p>· image_size: image size in nm</p> <p>· add_poisson_noise: 1 if poisson noise was added, 0 otherwise</p> <p>· frame_time: simulated frame time (required for poisson noise)</p> <p>· dwell_time: dwell time per pixel (required for poisson noise)</p> <p>· beam_current: beam current (required for poisson noise)</p> <p>· electrons_per_pixel: number of electrons per pixel</p> <p>· dose: electron dose in electrons per Å<sup>2</sup></p> <p>· add_scan_noise: 1 if scan noise was added, 0 otherwise</p> <p>· beam_misposition: parameter that describes how far the beam can be misplaced in pm (required for scan noise)</p> <p>· scan_noise: parameter that describes how far the beam can be misplaced in pix (required for scan noise)</p> <p>· add_focus_dependence: 1 if a focus effect is included, 0 otherwise</p> <p>· add_partial_coating: 1 if some TiO<sub>2</sub> particles were coated by a thin WO<sub>3</sub> film, 0 otherwise.</p> <p>· data_format: data format of the images, e.g. uint8</p> <p>For 3D reconstructions, the following information is added:</p> <p>· add_image_shifts, 1 if all images of the tilt series were shifted randomly by some pixel to account for misaligned images, 0 otherwise.</p> <p>· add_projection_noise: 1 if noise was added to projection angles, 0 otherwise.</p> <p>· N_SIRT: number of SIRT iterations for the 3D-reconstruction.</p> <p>· proj_angles: list of angles used for the projection directions of the tilt series in rad.</p> <p>For the 2D case, there are 24000 training images, 5500 validation images, 5500 test images, and their corresponding annotations. Aggregates and STEM images were obtained with the algorithm explained in the main work. The important data for CNN training is extracted from the files of individual aggregates and concluded in the subfolder <em>COCO</em>. For training, validation and test data there is a file <em>annotation_COCO.json</em> that includes all information required for the CNN training.</p> <p>For the 3D case, there are 100 simulated reconstructions that are divided into 80 training and 20 validation images within the training script.</p> <p>The zip archive <em>models.zip</em> includes the two networks that were trained, evaluated and used for the investigation in the manuscript. In the 2D case, network weights are stored in the file <em>2D_projection/logs/fit/20240209-095952/iter_60000.pth</em>. These weights can be loaded with the jupyter-notebook <em>2D_projection_prediction.ipynb</em>. Furthermore, a configuration file, which is required by the notebooks, is stored as <em>2D_projection/logs/fit</em> <em>20240209-095952/config_file.py</em>. In the 3D case, network weights are stored in <em>3D_reconstruction/model/weights_best.h5</em>. Also for this case, a configuration file is provided. The network can be loaded with the jupyter-notebook <em>3D_reconstruction_prediction.ipynb</em>.</p> <p>The zip archive <em>experiment_measurement.zip</em> includes the experimental 2D-projection images and the experimental 3D-reconstructions investigated in the manuscript. In the 3D case, we provide measured reconstructions as obtained by MATLAB and as transformed for the prediction with the CNN.</p> <p>The zip archive<em> experiment_prediction.zip</em> includes predictions and visualizations obtained by the 2D and 3D CNNs.</p> <p>The zip archive <em>simulation_measurement.zip</em> includes simulated 2D-projection images and simulated 3D-reconstructions that were not used for the network training. These simulations were used for evaluation of trained networks.</p> <p>The zip archive<em> simulation_prediction.zip</em> includes predictions and visualizations obtained by the 2D and 3D CNNs for the simulated data that was not used during the training process.</p> <p>In the zip archive <em>code.zip</em>, we provide several files with Python and MATLAB code that can be used for training, prediction and evaluation of the CNNs. A lot of information is provided within the comments and markdowns. For the application, it is required that the MMDetection toolbox and the StarDist3D framework are installed.</p> <p>· <em>2D_projection_training.py:</em> This Python script can be used for network training of the 2D Mask R-CNN after installation of the MMDetection toolbox.</p> <p>· <em>3D_reconstruction_training.py</em>: This Python script can be used for network training of the StarDist-3D network after installation of the StarDist package.</p> <p>· <em>2D_projection_prediction.ipynb</em>: This jupyter-notebook can be used for the application of a trained Mask R-CNN to experimental and simulated 2D-projection data.</p> <p>· <em>3D_reconstruction_prediction.ipynb</em>: This jupyter-notebook can be used for the application of a trained StarDist-3D network to experimental and simulated 3D-reconstruction data.</p> <p>· <em>Evaluation_experiment.m</em>: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D experimental evaluations.</p> <p>· <em>Evaluation_simulation.m</em>: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D evaluations of simulations.</p> <p>· <em>particle_detection_functions.py:</em> This Python script contains functions required by the jupyter-notebooks. Details can be found within the comments.</p> <p>· <em>ASTRA_CM_plot_results.m</em>: This MATLAB script provides functions for the visualization of experimental and simulated 3D-reconstructions.</p> <p>· <em>ASTRA_CM_particle_detection.m</em>: This MATLAB script is used for the quantitative evaluation of segmentations of 3D-reconstructions.</p> <p> </p> <p>There is no confidential data in this dataset. It is neither offensive, nor insulting or threatening.</p> <p>The dataset was generated to discriminate between TiO<sub>2 </sub>and WO<sub>3</sub> nanoparticles in STEM-images and STEM tomography reconstructions. It might be possible that it can discriminate between different materials if the STEM contrast is similar to the contrast of TiO<sub>2 </sub>and WO<sub>3</sub> but there is no guarantee.</p>
Calf Muscle Perfusion in Patients With Intermittent Claudication by 3D-reconstruction of MSOT (MSOT_IC_3D)
ClinicalTrials.gov study NCT05773534. IPD Sharing: Not stated. Countries: 1. Publications: 5.
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