# Brain Hemorrhage Extended (BHX): Bounding Box Extrapolation from Thick to Thin Slice CT Images

**Creators**:  

Eduardo Pontes Reis, Felipe Nascimento, Mateus Aranha, Fernando Mainetti Secol, Birajara Machado, Marcelo Felix, Anouk Stein, Edson Amaro

**Published**: July 29, 2020  

**Version**: 1.1

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## 📄 Citation

Reis, E. P., Nascimento, F., Aranha, M., Mainetti Secol, F., Machado, B., Felix, M., Stein, A., & Amaro, E. (2020). *Brain Hemorrhage Extended (BHX): Bounding box extrapolation from thick to thin slice CT images* (version 1.1). PhysioNet. https://doi.org/10.13026/9cft-hg92

### PhysioNet Standard Citation:

Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). *PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals*. Circulation [Online]. 101 (23), pp. e215–e220.

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## 🧠 Abstract

BHX is a publicly available dataset with bounding box annotations for five types of acute hemorrhage as an extension of the [qure.ai CQ500 dataset](http://headctstudy.qure.ai/dataset). The dataset is intended to aid in developing machine learning solutions for hemorrhage detection and localization.

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## 📌 Key Points

- **39,668** bounding boxes in **23,409** images annotated for hemorrhage.

- Built from the ~170k image **CQ500 dataset**.

- Bounding boxes extrapolated from sparse thick-slice labeling to thin-slice images.

- Supports machine learning applications for hemorrhage localization and diagnosis.

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## 🧬 Background

- Intracranial hemorrhage is a serious condition with a **40% one-month mortality rate**.

- Head CT is the primary imaging modality.

- Manual bounding box annotation is time-consuming; hence, extrapolation methods are used.

- No existing public dataset includes **localization data** with bounding boxes until BHX.

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## 🧪 Methods

- Based on the **CQ500 dataset** (491 scans, 205 hemorrhage-positive).

- Labeled by 3 neuroradiologists with varying experience.

- Matching between thick and thin series was done via DICOM tag: `Image Position (patient)`.

- Six types of hemorrhage labeled:  

  - **Intraparenchymal**  

  - **Subarachnoid**  

  - **Intraventricular**  

  - **Epidural**  

  - **Acute Subdural**  

  - **Chronic Subdural**

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## 🗃️ Data Description

### Annotation Stats:

- **6,283** manually labeled bounding boxes in **3,558** images.

- **39,668** extrapolated bounding boxes in **23,409** images.

### Dataset Versions:

1. `1_Initial_Manual_Labeling.csv`: Hand-drawn annotations on thick slices.

2. `2_Extrapolation_to_All_Series.csv`: Extrapolated to all corresponding series.

3. `3_Extrapolation_to_Selected_Series.csv`: Extrapolated only for selected soft-tissue thin-slice series.

### Columns:

- `SOPInstanceUID`: Unique DICOM image ID

- `SeriesInstanceUID`: DICOM series ID

- `StudyInstanceUID`: DICOM study ID

- `data`: Bounding box coordinates (X, Y, width, height)

- `labelName`: Hemorrhage type

- `labelType`: Source of image (thick-slices, thin-slices, or other)

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## 🔗 DICOM UID Mapping

- Annotations are linked via DICOM tag: `0008,0018 – SOP Instance UID`.

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## 📂 Original Images

- Hosted at: [http://headctstudy.qure.ai/dataset](http://headctstudy.qure.ai/dataset)

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## 🔍 Usage Notes

- Unique resource for **bounding-box annotated hemorrhage images**.

- Enables **benchmarking** and **development** of deep learning algorithms.

- Includes extrapolated labels, some of which may have minor inaccuracies.

- Future work should consider **interpolating bounding boxes** between slices for smoother transitions.

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## 🖼️ Visual Inspection

- Explore annotated images at:  

  [https://public.md.ai/annotator/project/Y2qr6vqv/workspace](https://public.md.ai/annotator/project/Y2qr6vqv/workspace)

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## 🙏 Acknowledgements

- **qure.ai** – for publishing the CQ500 dataset.  

- **MD.ai** – for providing the annotation platform.

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## ⚠️ Conflicts of Interest

- A.S. is employed by MD.ai, which provided the annotation platform.

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## 📚 References

1. van Asch C, et al. *The Lancet Neurology*, 2010.

2. Heit J, et al. *Journal of Stroke*, 2017.

3. Chang P, et al. *American Journal of Neuroradiology*, 2018.

4. Goldstein J, Gilson A. *Current Treatment Options in Neurology*, 2011.

5. Prevedello L, et al. *Radiology*, 2017.

6. Chilamkurthy S, et al. *The Lancet*, 2018.

7. Kuo W, et al. *PNAS*, 2019.

8. RSNA ICH Detection. [Kaggle](https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection)

9. Mirza S, Gokhale S. *IntechOpen*, 2017.

10. Osborn A, et al. *Osborn’s Brain*, Elsevier, 2018.

11. Weiss K, et al. *AJR*, 2011.

12. DICOM Standard. [dicomstandard.org](https://www.dicomstandard.org)

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