Abstract
Intracranial hemorrhage (ICH) is a pathological condition characterized by bleeding inside the skull or brain, which can be attributed to various factors. Identifying, localizing and quantifying ICH has important clinical implications, in a bleed-dependent manner. While deep learning techniques are widely used in medical image segmentation and have been applied to the ICH segmentation task, existing public ICH datasets do not support the multi-class segmentation problem. To address this, we develop the Brain Hemorrhage Segmentation Dataset (BHSD), which provides a 3D multi-class ICH dataset containing 192 volumes with pixel-level annotations and 2200 volumes with slice-level annotations across five categories of ICH. To demonstrate the utility of the dataset, we formulate a series of supervised and semi-supervised ICH segmentation tasks. We provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset. The dataset and checkpoint is available at https://github.com/White65534/BHSD.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Auer, L.M., et al.: Endoscopic surgery versus medical treatment for spontaneous intracerebral hematoma: a randomized study. J. Neurosurg. 70(4), 530–535 (1989)
Chen, X., Yuan, Y., Zeng, G., Wang, J.: Semi-supervised semantic segmentation with cross pseudo supervision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2613–2622 (2021)
Chilamkurthy, S., et al.: Development and validation of deep learning algorithms for detection of critical findings in head CT scans (2018)
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
Flanders, A.E., et al.: Construction of a machine learning dataset through collaboration: the RSNA 2019 brain CT hemorrhage challenge. Radiol. Artif. Intell. 2(3), e190211 (2020)
Frontera, J.A., et al.: Prediction of symptomatic vasospasm after subarachnoid hemorrhage: the modified fisher scale. Neurosurgery 59(1), 21–27 (2006)
Grønbæk, H., et al.: Liver cirrhosis, other liver diseases, and risk of hospitalisation for intracerebral haemorrhage: a danish population-based case-control study. BMC Gastroenterol. 8, 1–6 (2008)
Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin UNETR: swin transformers for semantic segmentation of brain tumors in MRI images. In: Crimi, A., Bakas, S. (eds.) MICCAI 2021. LNCS, vol. 12962, pp. 272–284. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08999-2_22
Hatamizadeh, A., et al.: UNETR: transformers for 3D medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022)
Hemphill, J.C., III., et al.: Guidelines for the management of spontaneous intracerebral hemorrhage: a guideline for healthcare professionals from the american heart association/american stroke association. Stroke 46(7), 2032–2060 (2015)
Howard, G., et al.: Risk factors for intracerebral hemorrhage: the reasons for geographic and racial differences in stroke (regards) study. Stroke 44(5), 1282–1287 (2013)
Hssayeni, M., Croock, M., Salman, A., Al-khafaji, H., Yahya, Z., Ghoraani, B.: Computed tomography images for intracranial hemorrhage detection and segmentation. Intracranial hemorrhage segmentation using a deep convolutional model. Data 5(1), 14 (2020)
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Larobina, M., Murino, L.: Medical image file formats. J. Digit. Imaging 27, 200–206 (2014)
Lee, H., Kim, M., Do, S.: Practical window setting optimization for medical image deep learning. arXiv preprint arXiv:1812.00572 (2018)
Li, X., et al.: The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: the instance challenge. arXiv preprint arXiv:2301.03281 (2023)
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
McCarron, M.O., Nicoll, J.A., Ironside, J.W., Love, S., Alberts, M.J., Bone, I.: Cerebral amyloid angiopathy-related hemorrhage: interaction of apoe \(\varepsilon \)2 with putative clinical risk factors. Stroke 30(8), 1643–1646 (1999)
Reis, E.P., et al.: Brain hemorrhage extended (BHX): bounding box extrapolation from thick to thin slice CT images. PhysioNe 101(23), e215-20 (2020)
Steiner, T., et al.: European stroke organisation (ESO) guidelines for the management of spontaneous intracerebral hemorrhage. Int. J. Stroke 9(7), 840–855 (2014)
Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Verma, V., et al.: Interpolation consistency training for semi-supervised learning. Neural Netw. 145, 90–106 (2022)
Vu, T.H., Jain, H., Bucher, M., Cord, M., Pérez, P.: Advent: adversarial entropy minimization for domain adaptation in semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2517–2526 (2019)
Wang, X., et al.: A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans. NeuroImage Clin. 32, 102785 (2021)
Xie, Y., Zhang, J., Shen, C., Xia, Y.: CoTr: efficiently bridging CNN and transformer for 3D medical image segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 171–180. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87199-4_16
Yushkevich, P.A., Gerig, G.: ITK-SNAP: an intractive medical image segmentation tool to meet the need for expert-guided segmentation of complex medical images. IEEE Pulse 8(4), 54–57 (2017)
Zhou, H.Y., Guo, J., Zhang, Y., Yu, L., Wang, L., Yu, Y.: nnFormer: interleaved transformer for volumetric segmentation. arXiv preprint arXiv:2109.03201 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, B. et al. (2024). BHSD: A 3D Multi-class Brain Hemorrhage Segmentation Dataset. In: Cao, X., Xu, X., Rekik, I., Cui, Z., Ouyang, X. (eds) Machine Learning in Medical Imaging. MLMI 2023. Lecture Notes in Computer Science, vol 14348. Springer, Cham. https://doi.org/10.1007/978-3-031-45673-2_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-45673-2_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45672-5
Online ISBN: 978-3-031-45673-2
eBook Packages: Computer ScienceComputer Science (R0)
