BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset

Biao Wu, Yutong Xie, Zeyu Zhang, Jinchao Ge, Kaspar Yaxley +4 more
8/22/2023
cs.CV

No official code found. Best implementation: White65534/BHSD (28 stars, last commit 2025-11-03).

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.

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Code Implementations(1)

White65534/BHSDCommunity100%

This is the official project webpage of BHSD (MLMI 2023).

281Aug 21, 202311 months agoNOASSERTION

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Cite this paper

@article{wu2023bhsd,
  title  = {BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset},
  author = {Biao Wu and Yutong Xie and Zeyu Zhang and Jinchao Ge and Kaspar Yaxley and Suzan Bahadir and Qi Wu and Yifan Liu and Minh-Son To},
  year   = {2023},
  eprint = {2308.11298},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.11298v2}
}

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