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O3ResNet: A deep learning based forecast system to predict local ground-level daily maximum 8-hour average ozone in rural and suburban environment: Data
No full textThis record contains data for the manuscript "O3ResNet: A deep learning based forecast system to predict local ground-level daily maximum 8-hour average ozone in rural and suburban environment" by L. H. Leufen, F. Kleinert and M. G. Schultz.We provide the processed input data, the forecasts made by O3ResNet, and the O3ResNet model.To have a better insight into the data and model, we provide a ready-to-run jupyter notebook to load and visualize our data and results. To run this notebook we rely on docker. Download the docker file (leufen-docker.tar.gz), the basic data file (leufen-data-base.tar.gz) and the model file (leufen-model.tar.gz) and follow the instructions (instructions.md and instructions.pdf) to load data, model, and the notebook. Note that changes made by the user to the notebook well be removed on exit as long the option "--rm" is present. When following the instructions, it is *not* required to unpack the input data files (leufen-data-.tar.gz). If you encount issues with disk space limits and docker, it is always possible to use a reduced number of data files or to unpack data for a single country and just parse them to the docker container. When using a windows host system, some commands provided in the instructions might slightly deviate
Deep learning based 3d reconstruction for phenotyping of wheat seeds: dataset
No full textWe present a new data set for 3d wheat seed reconstruction, propose a challenge “Wheat Seed 3d Reconstruction Challenge”, and provide baseline methods [1].
The dataset consists of 2964 seeds, split into 2520 seeds for training/validation and 444 for testing. Ground truth data for the test set is not provided, however, test results can be evaluated in the “Wheat Seed 3d Reconstruction Challenge” on https://helmholtz-data-challenges.de/.
Per seed there are:
(1) Point cloud, reconstructed from 36 images from turntable setup
(2) 36 projection matrices, projecting point cloud (2) back to corresponding images from turntable setup
(3) Top view taken from another camera
We provide raw (1) and preprocessed (5) versions of point clouds, and preprocessed versions of images from turntable setup (4). Raw images from turntable setup are available in the other data record on https://b2share.fz-juelich.de/ "Deep learning based 3d reconstruction for phenotyping of wheat seeds: dataset (with raw images)" (http://doi.org/10.34730/3541bf71388946b3a3a906fef7aed491).
=======================
Detailed content:
(1) raw_point_clouds/ -- .ply -- N points, N<70000, x-y-z coordinates, ascii format
(2) projection_matrices/ -- .txt -- 36x4x4 floats
(3) 2d_station_images/ -- .png -- 700x700, 24 bit, RGB color
(4) preprocessed_3d_station_images/ -- .tif -- stabilized and cropped to 373x200, 8 bit, grayscale
(5) preprocessed_gt_point_clouds/ --.ply – 2000 points, x-y-z coordinates, ascii format
(6) general_files/ -- additional files, files for competition submission
(1) consists of point clouds, one per seed. (5) consists of point clouds in 36 corresponding poses per seed.
Raw 3d station images from turntable setup were preprocessed, stabilized and resampled, which results in (4). Raw point clouds (1) were preprocessed (resampled) such that each of them contains 2000 points, lying in the fixed set of directions /general_files/directions.csv. Preprocessed point clouds (5) are convertible to triangular mesh using indices of vertex triplets /general_files/triangles.txt. A sphere sampled with these vertices and triangles is in /general_files/fibo_msh.ply.
The test set does not include point clouds, and has 3 views per seed: 0, 120 and 240 degrees.
Zero-padded integers XXXX are seed indices (here depicted 0000 and 0003). Zero-padded integers YYY in rotation_YYY are rotation angles in degrees: 0, 10, .. 350 degrees.
Indices of train and test sets are provided in general_files/indices_train.txt and general_files/indices_test.txt files. Volumes of raw point clouds of the train set are located in general_files/train_gt_volumes.csv.
Point cloud files .ply (1) and (5) were created with open3d python library, the header of the file is:
format ascii 1.0
element vertex ?????
property float x
property float y
property float z
end_header
These .ply are visualizable with, e.g., MeshLab (https://www.meshlab.net/) in windows.
There are two files in this data record: seed_dataset.zip and numpy_arrays.zip.
The file seed_dataset.zip contains the data, where each file is presented separately. The file numpy_arrays.zip contains numerical arrays, aggregating these separate files into multidimensional arrays readable with the python library numpy as np.load('file_name.npy').
Structure of the data after extraction of the numpy_arrays.zip into corresponding directories:
numpy_arrays
├── general_files
│ ├── directions.csv
│ ├── fibo_msh.ply
│ ├── indices_test.txt
│ ├── indices_train.txt
│ ├── train_gt_volumes.csv
│ └── triangles.txt
├── 2d_station_images_test.npy
├── 2d_station_images_train.npy
├── gt_points_train.npy
├── preprocessed_3d_station_images_test.npy
├── preprocessed_3d_station_images_train.npy
├── projection_matrices_test.npy
└── projection_matrices_train.npy
2d_station_images_test.npy (444, 3, 700, 700) uint8 (seed_idx_test, RGB_channel, height, width)
2d_station_images_train.npy (2520, 3, 700, 700) uint8 (seed_idx_train, RGB_channel, height, width)
gt_points_train.npy (2520, 36, 3, 2000) float32 (seed_idx_train, rotation_idx, x-y-z, point_idx)
preprocessed_3d_station_images_test.npy (444, 3, 200, 373) uint8 (seed_idx_test, ~rotation_idx~, height, width)
preprocessed_3d_station_images_train.npy (2520, 36, 200, 373) uint8 (seed_idx_train, rotation_idx, height, width)
projection_matrices_test.npy (444, 36, 4, 4) float32 (seed_idx_test, rotation_idx, row, column)
projection_matrices_train.npy (2520, 36, 4, 4) float32 (seed_idx_train, rotation_idx, row, column)
rotataion_idx [0, 1, .., 35] corresponds to [0, 10, .., 350] degrees.
~rotation_idx~~ [0, 1, 2] corresponds to [0, 120, 240] degrees.
seed_idx_test and seed_idx_train are in following order: general_files/indices_test.txt and general_files/indices_train.txt respectively.
Structure of the data after extraction of the seed_dataset.zip into corresponding directories:
seed_dataset
├── raw_point_clouds
│ └── train
│ └── 0000_Surface.ply
├── projection_matrices
│ ├── train
│ │ └── 0000_ProjectionMatrices.txt
│ └── test
│ └── 0003_ProjectionMatrices.txt
├── 2d_station_images
│ ├── train
│ │ └── 0000_2D.png
│ └── test
│ └── 0003_2D.png
├── preprocessed_3d_station_images
│ ├── train
│ │ └── 0000
│ │ ├── rotation_000.tif
│ │ ├── rotation_010.tif
│ │ ├── ...
│ │ └── rotation_350.tif
│ └── test
│ └── 0003
│ ├── rotation_000.tif
│ ├── rotation_120.tif
│ └── rotation_240.tif
├── preprocessed_gt_point_clouds
│ └── train
│ ├── 0000
│ ├── rotation_000.ply
│ ├── rotation_010.ply
│ ├── ...
│ └── rotation_350.ply
└── general_files
├── directions.csv
├── fibo_msh.ply
├── indices_test.txt
├── indices_train.txt
├── train_gt_volumes.csv
└── triangles.txt
It is mandatory to cite our paper when using this dataset:
[1] V.Cherepashkin, E.Yildiz, A.Fischbach, L.Kobbelt, and H. Scharr. Deep learning based 3d reconstruction for phenotyping of wheat seeds: a dataset, challenge, and baseline method. CVPPA at ICCV 2023, Paris, 2 October 202
CerebNet Segmentation Models for Cerebellar Sug-Segmentation
No full textQuantifying the volume of the cerebellum and its lobes is of profound interest in various neurodegenerative and acquired diseases. Especially for the most common spinocerebellar ataxias (SCA), for which the first antisense oligonculeotide-base gene silencing trial has recently started, there is an urgent need for quantitative, sensitive imaging markers at pre-symptomatic stages for stratification and treatment assessment. This work introduces CerebNet, a fully automated, extensively validated, deep learning method for the lobular segmentation of the cerebellum, including the separation of gray and white matter. For training, validation, and testing, T1-weighted images from 30 participants were manually annotated into cerebellar lobules and vermal sub-segments, as well as cerebellar white matter. CerebNet combines FastSurferCNN, a UNet-based 2.5D segmentation network, with extensive data augmentation, e.g. realistic non-linear deformations to increase the anatomical variety, eliminating additional preprocessing steps, such as spatial normalization or bias field correction. CerebNet demonstrates a high accuracy (on average 0.87 Dice and 1.742mm Robust Hausdorff Distance across all structures) outperforming state-of-the-art approaches. Furthermore, it shows high test-retest reliability (average ICC on OASIS and Kirby) as well as high sensitivity to disease effects, including the pre-ataxic stage of spinocerebellar ataxia type 3 (SCA3). CerebNet is compatible with FreeSurfer and FastSurfer and can analyze a 3D volume within seconds on a consumer GPU in an end-to-end fashion, thus providing an efficient and validated solution for assessing cerebellum sub-structure volumes. We make CerebNet available as source-code (https://github.com/Deep-MI/FastSurfer).Three files for trained models of the three different slicing directions
DeepRain Project presentations
No full textIn this entry a collection of internal publications, presentations, slides and reports from DeepRain. DeepRain is funded by the Bundesministerium für Bildung und Forschung (BMBF) under grant agreement 01 IS18047A-E
Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties - Source Code
No full textThis source code contains all methods that is being used in ozone mapping project. In addition, it contains scripts to run both explainable AI methods and methods used to study the impact of uncertainties.
Current developments can be tracked in the gitlab repository https://gitlab.jsc.fz-juelich.de/esde/machine-learning/ozone-mapping
This resource contains our complete source code in a zip archive(ozone-mapping.zip), a readme file (README.md) and setup directory(ozone-mapping-setup.zip) which contains requirements file to run in own system or on our cluster
DeepRain project presentations
No full textIn this entry a collection of internal publications, presentations, slides and reports from DeepRain. DeepRain is funded by the Bundesministerium für Bildung und Forschung (BMBF) under grant agreement 01 IS18047A-E
Global_Aerosol_OPP_profile_reanalysis_from_MERRA-2, vol.3
No full textSee Global_Aerosol_OPP_profile_reanalysis_from_MERRA-2, vol.1 for full description
Segmentation Models for 3D neuroanatomic segmentation
No full textWith this work, we provide a 3D whole brain segmentation models that outperforms the 2.5D approach FastSurferCNN. Furthermore, we explore the efficacy of spatial ensembling and its parameters. This resource contains the model files associated with final results of "Are 2.5D approaches superior to 3D deep networks in whole brain segmentation?" by Roy, Saikat; Kügler, David; Reuter, Martin to be published at the MIDL 2022 conference https://openreview.net/forum?id=Ob62JPB_CDF. These model files allow reproduction of our evaluation results and application of our models to other datasets. The accompanying source code is provided at https://github.com/Deep-MI/3d-neuro-seg .unensembled_model: Weights of the 3D architecture trained without "Spatial Ensembling" labeled "Ours (w/o SL)" in the paper.
ensembled_model_: Weights of the 3D architecture with all methodological options enabled labeled "Ours" in the paper. The index identifies the associated image region (image quadrant). Find further documentation w.r.t. image regions in model/QuadNet.py at https://github.com/Deep-MI/3d-neuro-seg (especially assign_nets_to_coords()
FAIR Digital Object
No full textInternal presentation in Jülich Supercomputing centre about FAIR Digital Object and its application for TOAR database