# BoneMet
The Breast Tumor Bone Metastasis (BoneMet) dataset, the first large-scale, publicly available, high-resolution medical resource specifically targeting BTBM for disease diagnosis, prognosis, advanced image processing, and treatment management. It offers over 50 terabytes of multi-modal medical data, including 2D X-ray images, 3D CT scans, and detailed biological data (e.g., medical records and bone quantitative analysis), collected from thousands of mice spanning from 2019 to 2024. Our BoneMet dataset is well-organized into six components, i.e., Rotation-X-Ray, Recon-CT, Seg-CT, Regist-CT, RoI-CT, and MiceMediRec. Thanks to its extensive data samples and meaningful our tireless efforts of image processing, organization and data labeling, this dataset BoneMet can be readily adopted to build versatile, large-scale AI models for managing BTBM diseases, which have been validated by our extensive experiments via various deep learning solutions either through self-supervised pre-training or supervised fine-tuning. To facilitate its easy access and wide dissemination, we have created the BoneMet package, providing three APIs that enable researchers to (i) flexibly process and download the BoneMet data filtered by specific time frames; and (ii) develop and train large-scale AI models for precise BTBM diagnosis and prognosis.
## Overview
The BoneMet dataset is the first *terabyte-sized* and publicly available breast tumor bone metastasis dataset, a collection of high-resolution, well-organized multi-angle rotational X-ray and CT images accompanied by detailed biological data for breast tumor bone metastasis diagnosis and prognosis.
- The `BoneMet` dataset is available at [Hugging Face](https://huggingface.co/datasets/BoneMet/BoneMet)
- The tutorial for the `BoneMet` dataset is available at [Github](https://github.com/BoneMet/BoneMet)
<!-- ## Tutorials
The tutorials for the BoneMet dataset are available at Google Colab, with their links listed below
- [Sentinel-2 Imagery Tutorial](https://colab.research.google.com/drive/1Tj69JdhO7aX8ks-4UWYvHrFm9GB1PNCd?usp=sharing)
- [WRF-HRRR Computed Dataset Tutorial](https://colab.research.google.com/drive/14l-JSNHtelawNu3kVG_ukTd2WUJpaZEc?usp=sharing)
- [USDA Crop Dataset Tutorial](https://colab.research.google.com/drive/1U-vFoRyLSb2l2Q67LeGbkUKTeRaHDkkK?usp=sharing)
-->
## The BoneMet Dataset
0ur BoneMet dataset is dataset is well-organized into six key components: Rotationa X-Ray Imagery(Rotation-X-Ray), Reconstructed CT Imagery (Recon-CT), Segmented CT Imagery(Seg-CT), Registered CT Imagery (Regist-CT), Region of Interest CT Imagery (RoI-CT), and Mice Medical Records (MiceMediRec), spanning from 2019 to 2024 (i.e., 5 years) across over 50 terabytes of multi-modal medical data, including 2D X-ray images, 3D CT scans, and detailed biological data (e.g., medical records and bone quantitative analysis), collected1 from thousands of mice.
<!--  -->
### Rotation-X-Ray
The Rotational X-Ray Imagery consists of 651,300 X-ray images of subjects with tumors and 676,000 X-ray images of subjects without tumors. Each image has a resolution of 4,032x4,032x1 pixels and a spatial resolution of 0.8°, captured at the hindlimb. This dataset has been aligned both spatially and temporally with the temporal resolution of 1 week, and it offers 2D X-ray images taken from multiple angles, from anterior (front) to lateral (side) and posterior (back) views,providing a comprehensive examination of the subject. The total size of this imagery is 20.93 TB.
### Recon-CT & Seg-CT
The Rotational X-Ray Imagery consists of 651,300 X-ray images of subjects with tumors and 676,000 X-ray images of subjects without tumors. Each image has a resolution of 4,032x4,032x1 pixels and a spatial resolution of 0.8°, captured at the hindlimb. This dataset has been aligned both spatially and temporally with the temporal resolution of 1 week, and it offers 2D X-ray images taken from multiple angles, from anterior (front) to lateral (side) and posterior (back) views,providing a comprehensive examination of the subject. The total size of this imagery is 20.93 TB.
These 3D CTs of tibiae are isolated from the 3D CT scans of hindlimb in the Recon-CT imagery, as illustrated on the right side of Figure 3. This component includes 3,005 segmented CT scans of subjects with tumors and 7,205 segmented CT scans of subjects without tumors. Each scan is composed of approximately 1,700±200 2D slices with an image resolution of approximately 700±50x900±80x1 pixels. The size of this dataset is 1.53 TB.
### Regist-CT
This Regist-CT dataset includes registered 3D CT scans of tibiae taken at various time points and from different animals, aligned to a reference. This component includes 3,005 registered CT scans of subjects with tumors and 7,205 registered CT scans of subjects without tumors. Each scan is composed of 1,538 2D slices with an image resolution of 509x539x1 pixels. The size of this dataset is 0.18 TB.
### RoI-CT
This imagery focuses on the proximal end sections of the registered tibiae, where the effects of metastasis are most pronounced. The RoI-CT imagery comprises 300 2D slices below the proximal tibia-fibula junction, with overlaid registered CT scans aligned to their baseline (week 0). In each 2D slice, light gray represents the reserved bone in the sequential scans, white indicates bone formation where non-bone pixels at week 0 later became bone, and dark gray signifies bone resorption where bone pixels at week 0 later became non-bone. This component includes 3,005 CT scans of the proximal end sections of registered tibiae with tumors and 7,205 CT scans of that without tumors. Each 2D slice has the image resolution of 509x539x1 pixels. The size of this dataset is 8.00 GB.
### MiceMediRec
The Mice Medical Records includes the number of mice of 501 with tumor and of 520 without tumor and results from detailed medical records such as experiment date, animal ID, age, body weight, mouse strain (or genotype), sex, and specific metastatic tumor sites, and quantitative analyses of bone from CTs, FE simulations, and mechanical testing, offering a comprehensive overview of the animals, bones, and their disease conditions. The size of this dataset is 9.44 MB.
## Pipeline
This repository including three types of APIs: i.e., (1) CT Image Segmentation, (2) CT Image Registration, and (3) RoI-based CT Image Cropping, at the Python Package Index(PyPI), for public release to facilitate our dataset's ease access. The detials of three APIs and their usage examples are listed as follows:
- **CT Image Segmentation API**: This API provides a simple interface to segment the 3D Reconstructed CT (Recon-CT) images into separate CT scans for the spine, left tibia, left femur, right tibia, and right femur. It can handle individual or batched segmentation of the Recon-CT scans. The API reads the 3D CT scans, identifies the appropriate indices to split the images, and saves the segmented scans to the specified output paths. Given the time point (e.g., the week after tumor inoculation), the input folder path, and the output folder path.
- **CT Image Registration API**: This API helps researchers with the tibia registration on Seg-CT dataset. It can handle individual or batched registration of the segmented tibiae CTs. The API loads the reference and target CT scans, performs initial transformation, and registers the target CT scan to the reference CT scan. Then the registered CT scan and the transformation are saved to the specific output folder. Given the time point (e.g., the week after tumor inoculation), the slices range of reference and target subjects, the input folder path, the reference folder path, and the output folder path.
- **RoI-based CT Image Cropping API**: This API provides a simple interface to crop the region of interest (tibia proximal end) on Regist-CT dataset. It can handle batched cropping of the Regist-CT dataset. The API reads the overlapped 3D Regist-CT composite processed by our python package, identifies the proximal tibia-fibular junction, selects appropriate indices to split the images, and saves the cropped to the specified output paths. Given the input folder path, the output folder path, and index of the first selected slice below the tibia-femoral junction.
## Installation
Researchers and practitioners can install the latest version of BoneMet with the following commands:
```python
# Create and activate a conda environment
conda create -n BoneMet_api python=3.10
conda activate BoneMet_api
# Install the latest version of BoneMet
pip install BoneMet
# Slove the ecCodes library dependency issue
pip install SimpleITK
```
### BoneMet API Examples
- **Example 1: A CT Image Segmentation API Example for Tibiae Batch Segmentation**
Given the time and ID, the following code presents how to utilize the **CT Image Segmentation API** to segment the left and right tibiae from the hindlimb in the Recon-CT dataset, either individually or in batches:
```python
config = {
"week": " week 0",
"masterfolder": r"F:\Recon-CT\week 0",
"masterout": r"F:\Seg-CT\week 0"
}
splitter = ReconCTSegmentation(config)
# Split a single image
input_folder = r"F:\Recon-CT\week 0\871"
image_title = "871"
splitter.split_image(input_folder, image_title, config["masterout"])
# Split multiple images
for folder in os.listdir(config["masterfolder"]):
if folder[0:10] in [871, 872, 873, ...]:
input_folder = os.path.join(config["masterfolder"], folder)
image_title = os.path.basename(folder)[0:12]
splitter.split_image(input_folder, image_title, config["masterout"])
```
- **Example 2: A CT Image Registration API Example for Tibiae Batch Registration**
Given the reference and location for alignment, the following code shows how to use the **CT Image Registration API** to obtain the Regist-CT data and store in the local machine in a user-friendly format, either individually or in batches:
```python
import os
import re
import SimpleITK as sitk
import concurrent.futures
config = {
"workspace": r"F:\Seg-CT\week 0",
"outputdir": r"F:\Regist-CT\week 0",
"refdir": r"F:\reference",
"img_z_range": [None, None],
"ref_z_range": [None, None],
"initial_transform_angles": [np.pi * i / 16 for i in range(-16, 10)],
"BASELINE_REG": True, # week 0 (True) or sequencial scans (False)
}
# Initialize the registration instance
registration = CTRegistration(config)
# Register a single CT scan
input_folder = r"F:\Seg-CT\week 0"
ct_id = "871 week 0 left tibia"
week = 0
output_folder = config["outputdir"]
registration.register_ct(input_folder, ct_id, week, output_folder)
# Register a batch of CT scans
input_folder = r"F:\Seg-CT\week 0"
ct_ids = ["871 week 0 left tibia", "871 week 0 right tibia", "872 week 11 left tibia", ...]
week = 0
output_folder = config["outputdir"]
registration.batch_register(input_folder, ct_ids, week, output_folder)
```
- **Example 3: A RoI-based CT Image Cropping API Example for Using the Overlapped Regist-CT Data to Crop the Proximal End of Tibiae in a batch**
The following code presents a example of tibiae proximal end cropping from overlapped Regist-CT dataset starting at the proxiaml tibia-fibula junction. The overlapped composites data is operated by our python tool -- mkcomposite.py:
```python
import os
import cv2
import numpy as np
from skimage import io
config = {
"foldername": "selected 300 slices below proximal Tibia-fibular junction",
"first_slice_selected": "first slice selected",
"last_slice_selected": "last slice selected",
"first_slice_selected_below_t-f_junction": 0 # Index of the first selected slice below the tibia-fibular junction
}
# Initialize the RoICropper
cropper = RoICompositeCropper(config)
# Crop the RoI from CT images
input_folder = r"F:\Regist-CT\Tibia w0w5composite"
output_folder = os.path.join(input_folder, config["foldername"])
first_slice_selected = config["first_slice_selected"]
last_slice_selected = config["last_slice_selected"]
first_slice_below_tf_junction = config["first_slice_selected_above_t-f_junction"]
cropper.crop_roi(input_folder, output_folder, first_slice_selected, last_slice_selected, first_slice_below_tf_junction)
```
# Using BoneMet with other Experiments
We will be demonstrating how our dataset BoneMet can be used in other experiments, specifically reconstructing an x-ray with medNerf.
## Loading the Dataset
Begin by loading the BoneMet dataset. You can load the entire dataset with
```
from datasets import load_dataset
dataset = laod_dataset("BoneMet/BoneMet")
```
You can also specify certain directories if you only need a certain subset of the dataset. When reconstructing x-ray's with medNerf, we will only need x-ray data:
```
dataset = load_dataset("BoneMet/BoneMet", data_dir = "Imagery_Dataset/1. Rotation-X-ray/851/week 0")
```
The dataset will be cached in `C:\Users\<User>\.cache\huggingface\hub\datasets--BoneMet--BoneMet`.
## Using with MedNerf
After downloading the dataset, open the `knee.yaml` file found in the configs folder. Change the `datadir` field to the file location of the downloaded dataset. Now, in the `graf-main` directory you can run the `train.py` file with BoneMet data. Use the following command to run the script.
```
python train.py ./configs/knee.yaml
```
## License
BoneMet has a [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) license.
Raw data
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"description": "# BoneMet\r\n\r\nThe Breast Tumor Bone Metastasis (BoneMet) dataset, the first large-scale, publicly available, high-resolution medical resource specifically targeting BTBM for disease diagnosis, prognosis, advanced image processing, and treatment management. It offers over 50 terabytes of multi-modal medical data, including 2D X-ray images, 3D CT scans, and detailed biological data (e.g., medical records and bone quantitative analysis), collected from thousands of mice spanning from 2019 to 2024. Our BoneMet dataset is well-organized into six components, i.e., Rotation-X-Ray, Recon-CT, Seg-CT, Regist-CT, RoI-CT, and MiceMediRec. Thanks to its extensive data samples and meaningful our tireless efforts of image processing, organization and data labeling, this dataset BoneMet can be readily adopted to build versatile, large-scale AI models for managing BTBM diseases, which have been validated by our extensive experiments via various deep learning solutions either through self-supervised pre-training or supervised fine-tuning. To facilitate its easy access and wide dissemination, we have created the BoneMet package, providing three APIs that enable researchers to (i) flexibly process and download the BoneMet data filtered by specific time frames; and (ii) develop and train large-scale AI models for precise BTBM diagnosis and prognosis.\r\n\r\n\r\n## Overview\r\n\r\nThe BoneMet dataset is the first *terabyte-sized* and publicly available breast tumor bone metastasis dataset, a collection of high-resolution, well-organized multi-angle rotational X-ray and CT images accompanied by detailed biological data for breast tumor bone metastasis diagnosis and prognosis. \r\n\r\n- The `BoneMet` dataset is available at [Hugging Face](https://huggingface.co/datasets/BoneMet/BoneMet)\r\n\r\n- The tutorial for the `BoneMet` dataset is available at [Github](https://github.com/BoneMet/BoneMet)\r\n\r\n\r\n\r\n<!-- ## Tutorials\r\n\r\nThe tutorials for the BoneMet dataset are available at Google Colab, with their links listed below\r\n\r\n- [Sentinel-2 Imagery Tutorial](https://colab.research.google.com/drive/1Tj69JdhO7aX8ks-4UWYvHrFm9GB1PNCd?usp=sharing)\r\n- [WRF-HRRR Computed Dataset Tutorial](https://colab.research.google.com/drive/14l-JSNHtelawNu3kVG_ukTd2WUJpaZEc?usp=sharing)\r\n\r\n- [USDA Crop Dataset Tutorial](https://colab.research.google.com/drive/1U-vFoRyLSb2l2Q67LeGbkUKTeRaHDkkK?usp=sharing)\r\n -->\r\n\r\n\r\n## The BoneMet Dataset\r\n\r\n0ur BoneMet dataset is dataset is well-organized into six key components: Rotationa X-Ray Imagery(Rotation-X-Ray), Reconstructed CT Imagery (Recon-CT), Segmented CT Imagery(Seg-CT), Registered CT Imagery (Regist-CT), Region of Interest CT Imagery (RoI-CT), and Mice Medical Records (MiceMediRec), spanning from 2019 to 2024 (i.e., 5 years) across over 50 terabytes of multi-modal medical data, including 2D X-ray images, 3D CT scans, and detailed biological data (e.g., medical records and bone quantitative analysis), collected1 from thousands of mice.\r\n\r\n<!--  -->\r\n\r\n\r\n### Rotation-X-Ray\r\n\r\nThe Rotational X-Ray Imagery consists of 651,300 X-ray images of subjects with tumors and 676,000 X-ray images of subjects without tumors. Each image has a resolution of 4,032x4,032x1 pixels and a spatial resolution of 0.8°, captured at the hindlimb. This dataset has been aligned both spatially and temporally with the temporal resolution of 1 week, and it offers 2D X-ray images taken from multiple angles, from anterior (front) to lateral (side) and posterior (back) views,providing a comprehensive examination of the subject. The total size of this imagery is 20.93 TB. \r\n\r\n### Recon-CT & Seg-CT\r\n\r\nThe Rotational X-Ray Imagery consists of 651,300 X-ray images of subjects with tumors and 676,000 X-ray images of subjects without tumors. Each image has a resolution of 4,032x4,032x1 pixels and a spatial resolution of 0.8°, captured at the hindlimb. This dataset has been aligned both spatially and temporally with the temporal resolution of 1 week, and it offers 2D X-ray images taken from multiple angles, from anterior (front) to lateral (side) and posterior (back) views,providing a comprehensive examination of the subject. The total size of this imagery is 20.93 TB. \r\n\r\nThese 3D CTs of tibiae are isolated from the 3D CT scans of hindlimb in the Recon-CT imagery, as illustrated on the right side of Figure 3. This component includes 3,005 segmented CT scans of subjects with tumors and 7,205 segmented CT scans of subjects without tumors. Each scan is composed of approximately 1,700±200 2D slices with an image resolution of approximately 700±50x900±80x1 pixels. The size of this dataset is 1.53 TB.\r\n\r\n### Regist-CT\r\n\r\nThis Regist-CT dataset includes registered 3D CT scans of tibiae taken at various time points and from different animals, aligned to a reference. This component includes 3,005 registered CT scans of subjects with tumors and 7,205 registered CT scans of subjects without tumors. Each scan is composed of 1,538 2D slices with an image resolution of 509x539x1 pixels. The size of this dataset is 0.18 TB. \r\n\r\n### RoI-CT\r\n\r\nThis imagery focuses on the proximal end sections of the registered tibiae, where the effects of metastasis are most pronounced. The RoI-CT imagery comprises 300 2D slices below the proximal tibia-fibula junction, with overlaid registered CT scans aligned to their baseline (week 0). In each 2D slice, light gray represents the reserved bone in the sequential scans, white indicates bone formation where non-bone pixels at week 0 later became bone, and dark gray signifies bone resorption where bone pixels at week 0 later became non-bone. This component includes 3,005 CT scans of the proximal end sections of registered tibiae with tumors and 7,205 CT scans of that without tumors. Each 2D slice has the image resolution of 509x539x1 pixels. The size of this dataset is 8.00 GB. \r\n\r\n### MiceMediRec\r\n\r\nThe Mice Medical Records includes the number of mice of 501 with tumor and of 520 without tumor and results from detailed medical records such as experiment date, animal ID, age, body weight, mouse strain (or genotype), sex, and specific metastatic tumor sites, and quantitative analyses of bone from CTs, FE simulations, and mechanical testing, offering a comprehensive overview of the animals, bones, and their disease conditions. The size of this dataset is 9.44 MB. \r\n\r\n\r\n## Pipeline\r\n\r\nThis repository including three types of APIs: i.e., (1) CT Image Segmentation, (2) CT Image Registration, and (3) RoI-based CT Image Cropping, at the Python Package Index(PyPI), for public release to facilitate our dataset's ease access. The detials of three APIs and their usage examples are listed as follows:\r\n\r\n- **CT Image Segmentation API**: This API provides a simple interface to segment the 3D Reconstructed CT (Recon-CT) images into separate CT scans for the spine, left tibia, left femur, right tibia, and right femur. It can handle individual or batched segmentation of the Recon-CT scans. The API reads the 3D CT scans, identifies the appropriate indices to split the images, and saves the segmented scans to the specified output paths. Given the time point (e.g., the week after tumor inoculation), the input folder path, and the output folder path. \r\n\r\n- **CT Image Registration API**: This API helps researchers with the tibia registration on Seg-CT dataset. It can handle individual or batched registration of the segmented tibiae CTs. The API loads the reference and target CT scans, performs initial transformation, and registers the target CT scan to the reference CT scan. Then the registered CT scan and the transformation are saved to the specific output folder. Given the time point (e.g., the week after tumor inoculation), the slices range of reference and target subjects, the input folder path, the reference folder path, and the output folder path.\r\n\r\n- **RoI-based CT Image Cropping API**: This API provides a simple interface to crop the region of interest (tibia proximal end) on Regist-CT dataset. It can handle batched cropping of the Regist-CT dataset. The API reads the overlapped 3D Regist-CT composite processed by our python package, identifies the proximal tibia-fibular junction, selects appropriate indices to split the images, and saves the cropped to the specified output paths. Given the input folder path, the output folder path, and index of the first selected slice below the tibia-femoral junction.\r\n\r\n\r\n## Installation\r\n\r\nResearchers and practitioners can install the latest version of BoneMet with the following commands:\r\n\r\n```python\r\n# Create and activate a conda environment\r\nconda create -n BoneMet_api python=3.10\r\nconda activate BoneMet_api\r\n\r\n# Install the latest version of BoneMet\r\npip install BoneMet\r\n\r\n# Slove the ecCodes library dependency issue\r\npip install SimpleITK\r\n```\r\n\r\n\r\n### BoneMet API Examples\r\n\r\n- **Example 1: A CT Image Segmentation API Example for Tibiae Batch Segmentation**\r\n\r\n Given the time and ID, the following code presents how to utilize the **CT Image Segmentation API** to segment the left and right tibiae from the hindlimb in the Recon-CT dataset, either individually or in batches:\r\n\r\n```python\r\nconfig = {\r\n \"week\": \" week 0\",\r\n \"masterfolder\": r\"F:\\Recon-CT\\week 0\",\r\n \"masterout\": r\"F:\\Seg-CT\\week 0\"\r\n}\r\n\r\nsplitter = ReconCTSegmentation(config)\r\n\r\n# Split a single image\r\ninput_folder = r\"F:\\Recon-CT\\week 0\\871\"\r\nimage_title = \"871\"\r\nsplitter.split_image(input_folder, image_title, config[\"masterout\"])\r\n\r\n# Split multiple images\r\nfor folder in os.listdir(config[\"masterfolder\"]):\r\n if folder[0:10] in [871, 872, 873, ...]: \r\n input_folder = os.path.join(config[\"masterfolder\"], folder)\r\n image_title = os.path.basename(folder)[0:12]\r\n splitter.split_image(input_folder, image_title, config[\"masterout\"])\r\n```\r\n\r\n\r\n\r\n- **Example 2: A CT Image Registration API Example for Tibiae Batch Registration**\r\n\r\n Given the reference and location for alignment, the following code shows how to use the **CT Image Registration API** to obtain the Regist-CT data and store in the local machine in a user-friendly format, either individually or in batches:\r\n\r\n```python\r\nimport os\r\nimport re\r\nimport SimpleITK as sitk\r\nimport concurrent.futures\r\n\r\nconfig = {\r\n\"workspace\": r\"F:\\Seg-CT\\week 0\",\r\n\"outputdir\": r\"F:\\Regist-CT\\week 0\",\r\n\"refdir\": r\"F:\\reference\",\r\n\"img_z_range\": [None, None],\r\n\"ref_z_range\": [None, None],\r\n\"initial_transform_angles\": [np.pi * i / 16 for i in range(-16, 10)],\r\n\"BASELINE_REG\": True, # week 0 (True) or sequencial scans (False)\r\n}\r\n\r\n# Initialize the registration instance\r\nregistration = CTRegistration(config)\r\n\r\n# Register a single CT scan\r\ninput_folder = r\"F:\\Seg-CT\\week 0\"\r\nct_id = \"871 week 0 left tibia\"\r\nweek = 0\r\noutput_folder = config[\"outputdir\"]\r\nregistration.register_ct(input_folder, ct_id, week, output_folder)\r\n\r\n# Register a batch of CT scans\r\ninput_folder = r\"F:\\Seg-CT\\week 0\"\r\nct_ids = [\"871 week 0 left tibia\", \"871 week 0 right tibia\", \"872 week 11 left tibia\", ...]\r\nweek = 0\r\noutput_folder = config[\"outputdir\"]\r\nregistration.batch_register(input_folder, ct_ids, week, output_folder)\r\n\r\n```\r\n\r\n\r\n\r\n- **Example 3: A RoI-based CT Image Cropping API Example for Using the Overlapped Regist-CT Data to Crop the Proximal End of Tibiae in a batch** \r\n\r\nThe following code presents a example of tibiae proximal end cropping from overlapped Regist-CT dataset starting at the proxiaml tibia-fibula junction. The overlapped composites data is operated by our python tool -- mkcomposite.py:\r\n\r\n```python\r\nimport os\r\nimport cv2\r\nimport numpy as np\r\nfrom skimage import io\r\n\r\nconfig = {\r\n\"foldername\": \"selected 300 slices below proximal Tibia-fibular junction\",\r\n\"first_slice_selected\": \"first slice selected\",\r\n\"last_slice_selected\": \"last slice selected\",\r\n\"first_slice_selected_below_t-f_junction\": 0 # Index of the first selected slice below the tibia-fibular junction\r\n}\r\n\r\n# Initialize the RoICropper\r\ncropper = RoICompositeCropper(config)\r\n\r\n# Crop the RoI from CT images\r\ninput_folder = r\"F:\\Regist-CT\\Tibia w0w5composite\"\r\noutput_folder = os.path.join(input_folder, config[\"foldername\"])\r\nfirst_slice_selected = config[\"first_slice_selected\"]\r\nlast_slice_selected = config[\"last_slice_selected\"]\r\nfirst_slice_below_tf_junction = config[\"first_slice_selected_above_t-f_junction\"]\r\n\r\ncropper.crop_roi(input_folder, output_folder, first_slice_selected, last_slice_selected, first_slice_below_tf_junction)\r\n```\r\n\r\n# Using BoneMet with other Experiments\r\n\r\nWe will be demonstrating how our dataset BoneMet can be used in other experiments, specifically reconstructing an x-ray with medNerf.\r\n\r\n\r\n## Loading the Dataset\r\nBegin by loading the BoneMet dataset. You can load the entire dataset with \r\n```\r\nfrom datasets import load_dataset\r\ndataset = laod_dataset(\"BoneMet/BoneMet\")\r\n```\r\nYou can also specify certain directories if you only need a certain subset of the dataset. When reconstructing x-ray's with medNerf, we will only need x-ray data:\r\n```\r\ndataset = load_dataset(\"BoneMet/BoneMet\", data_dir = \"Imagery_Dataset/1. Rotation-X-ray/851/week 0\")\r\n```\r\nThe dataset will be cached in `C:\\Users\\<User>\\.cache\\huggingface\\hub\\datasets--BoneMet--BoneMet`. \r\n## Using with MedNerf\r\n\r\nAfter downloading the dataset, open the `knee.yaml` file found in the configs folder. Change the `datadir` field to the file location of the downloaded dataset. Now, in the `graf-main` directory you can run the `train.py` file with BoneMet data. Use the following command to run the script.\r\n```\r\npython train.py ./configs/knee.yaml\r\n```\r\n\r\n## License\r\n\r\nBoneMet has a [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) license.\r\n",
"bugtrack_url": null,
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"version": "0.0.9",
"project_urls": {
"Homepage": "https://github.com/BoneMet/BoneMet"
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