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LTER Zöbelboden, Austria, Forest Inventory and Tree biomass data 1993-2019|森林生态数据集|生物量研究数据集
收藏Mendeley Data2024-01-31 更新2024-06-29 收录
下载链接：
https://b2share.eudat.eu/records/d147da761fdd47cea6833178ba0597cd
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链接失效反馈资源简介：
Long-term forest inventory and aboveground tree biomass data from LTER Zöbelboden Austria (1993-2019). The dataset contains the forest inventory parameter (tree species, tree vitality status, diameter at breast height, tree height) as well as stem, branch, foliage/needle and total aboveground biomass of the trees within the permanent inventory plots covering the LTER Zöbelboden. The forest inventories were conducted in 1993, 2005, 2010, 2014 and 2019.
创建时间：
2024-01-31
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didsr/tsynth
--- license: cc0-1.0 task_categories: - image-classification - image-segmentation tags: - medical pretty_name: T-SYNTH size_categories: - 1K<n<10K --- # T-SYNTH <!-- Provide a quick summary of the dataset. --> T-SYNTH is a synthetic digital breast tomosynthesis (DBT) dataset with four breast fibroglandular density distributions imaged using Monte Carlo x-ray simulations with the publicly available [Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE)](https://github.com/DIDSR/VICTRE) toolkit. ## Dataset Details The dataset has the following characteristics: * Breast density: dense, heterogeneously dense, scattered, fatty * Mass radius (mm): 5.00, 7.00, 9.00 * Mass density: 1.0, 1.06, 1.1 (ratio of mass radiodensity to that of fibroglandular tissue) ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [Christopher Wiedeman](https://www.linkedin.com/in/christopher-wiedeman-a0b01014b), [Anastasiia Sarmakeeva](https://www.linkedin.com/in/anastasiia-sarmakeeva/), [Elena Sizikova](https://esizikova.github.io/), [Daniil Filienko](https://www.linkedin.com/in/daniil-filienko-800160215/), [Miguel Lago](https://www.linkedin.com/in/milaan/), [Jana Gut Delfino](https://www.linkedin.com/in/janadelfino/), [Aldo Badano](https://www.linkedin.com/in/aldobadano/) - **License:** Creative Commons 1.0 Universal License (CC0) ## Data Acquisition Details **Imaging Modality:** Paired 2D digital mammography (DM) and 3D digital breast tomosynthesis (DBT) images. The DBT images are projected into C-VIEW via the method of (Klein, 2023). **Manufacturer and Model:** Replica of the Siemens detector based on MC-GPU (Badal and Badano, 2009). **Demographics:** All breast phantoms are synthetic and do not represent real human subjects. **Cohort Description:** 9,000 synthetic digital breast tomosynthesis (DBT) samples, distributed across four breast density categories: | Breast Density | Fatty | Scattered | Hetero | Dense | **Total** | | --------- | --------- | --------- | ------- | ------- | --------- | | **Les.-free / Les.-present** | 1350/1350 | 1350/1350 | 900/900 | 900/900 | 4500/4500 | **Annotation Protocols:** Lesion masks and bounding boxes were generated automatically from the phantom. **Data Format and Structure:** Image files are in .raw format. ### Dataset Sources <!-- Provide the basic links for the dataset. --> - **Code:** [https://github.com/DIDSR/tsynth-release](https://github.com/DIDSR/tsynth-release) ## Intended Use <!-- Address questions around how the dataset is intended to be used. --> T-SYNTH is intended to facilitate testing of AI with pre-computed synthetic digital breast tomosynthesis (DBT) data, complementing the M-SYNTH synthetic mammography dataset. ## Ethical Considerations This work is using synthetically generated data and does not include any real patient-identifiable information. Publication of synthetic data aims to promote transparency, reproducibility, and fairness in medical AI research. We recommend avoiding training models only on synthetic data without appropriate validation. ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> Directory layout: ``` T-SYNTH/data/ ├── cview ├── embed_metadata ├── pretrained_models ├── results └── volumes_subset ``` Descriptions: * **`cview/`** -- contains T-SYNTH C-VIEW images. * **`embed_metadata/`** -- Configuration files needed to reproduce experiments. * **`pretrained_models/`** -- Pretrained models for ```DBT```, ```DM``` and ```diffusion``` experiments to reproduce results from the paper. Note to reproduce you need files from **`embed_metadata/`**. * **`results/`** -- Output data and plots used in the paper (see [T-SYNTH repository](https://github.com/DIDSR/tsynth-release/tree/main/code/notebooks)). Description for each experiment could be found [here](https://github.com/DIDSR/tsynth-release/blob/main/code/README.md#experiment-configuration-map). * **`volumes_subset/`** -- example of volumetric data. The full data set will be released later due to volume. The data is organized by lesion size, breast density and lesion density. Folder names follow the convention: ```output_cview_det_Victre/device_data_VICTREPhantoms_spic_[LESION_DENSITY]/[BREAST_DENSITY]/2/[LESION_SIZE]/SIM.zip```. Example path in `volumes_subset`: ``` device_data_VICTREPhantoms_spic_1.1/fatty/2/5.0/SIM/D2_5.0_fatty.1/1/ ├── reconstruction1.loc # Lesion coordinates ├── reconstruction1.mhd # Metadata (raw format) ├── reconstruction1.raw # Raw image data └── reconstruction1_mask.h5 # Pixel-level segmentation masks for lesions and tissues ``` ## How to use it The description how to use it could be found [here](https://github.com/DIDSR/tsynth-release/blob/main/code/README.md). ## Citation ``` @article{t-synth, title={T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images}, author={Christopher Wiedeman, Anastasiia Sarmakeeva, Elena Sizikova, Daniil Filienko, Miguel Lago, Jana G. Delfino, Aldo Badano}, journal={}, volume={}, pages={}, year={2025} } ``` ## Related Links 1. [Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE)](https://cdrh-rst.fda.gov/victre-silico-breast-imaging-pipeline). 2. [M-SYNTH: A Dataset for the Comparative Evaluation of Mammography AI](https://cdrh-rst.fda.gov/m-synth-dataset-comparative-evaluation-mammography-ai). 6. A. Kim*, N. Saharkhiz*, E. Sizikova*, M. Lago, B. Sahiner, J. G. Delfino, A. Badano. [S-SYNTH: Knowledge-Based, Synthetic Generation of Skin Images](https://github.com/DIDSR/ssynth-release). MICCAI 2024. 4. [FDA Catalog of Regulatory Science Tools to Help Assess New Medical Devices](https://www.fda.gov/medical-devices/science-and-research-medical-devices/catalog-regulatory-science-tools-help-assess-new-medical-devices).
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