# Intracranial Hemorrhage Segmentation Using Deep Convolutional Model

arXiv: https://arxiv.org/abs/1910.08643 · HTML: https://mlsanity.com/paper/1910.08643/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2019.

## Built on by (most cited first, 29 of 29)

- [A Systematic Collection of Medical Image Datasets for Deep Learning](https://mlsanity.com/paper/2106.12864/) (2021)
- [Constrained unsupervised anomaly segmentation](https://mlsanity.com/paper/2203.01671/) (2022)
- [Large-scale Long-tailed Disease Diagnosis on Radiology Images](https://mlsanity.com/paper/2312.16151/) (2023)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [A co-evolving agentic AI system for medical imaging analysis](https://mlsanity.com/paper/2509.20279/) (2025)
- [MediSee: Reasoning-based Pixel-level Perception in Medical Images](https://mlsanity.com/paper/2504.11008/) (2025)
- [Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learning](https://mlsanity.com/paper/2304.04902/) (2023)
- [VoxelPrompt: A Vision Agent for End-to-End Medical Image Analysis](https://mlsanity.com/paper/2410.08397/) (2024)
- [SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage Segmentation](https://mlsanity.com/paper/2311.08190/) (2023)
- [SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Prompts](https://mlsanity.com/paper/2403.13258/) (2024)
- [Transformers-based architectures for stroke segmentation: A review](https://mlsanity.com/paper/2403.18637/) (2024)
- [Voxels Intersecting along Orthogonal Levels Attention U-Net for Intracerebral Haemorrhage Segmentation in Head CT](https://mlsanity.com/paper/2208.06313/) (2022)
- [Cut-Paste Consistency Learning for Semi-Supervised Lesion Segmentation](https://mlsanity.com/paper/2210.00191/) (2022)
- [Weakly Supervised Intracranial Hemorrhage Segmentation with YOLO and an Uncertainty Rectified Segment Anything Model](https://mlsanity.com/paper/2407.20461/) (2024)
- [PHE-SICH-CT-IDS: A Benchmark CT Image Dataset for Evaluation Semantic Segmentation, Object Detection and Radiomic Feature Extraction of Perihematomal Edema in Spontaneous Intracerebral Hemorrhage](https://mlsanity.com/paper/2308.10521/) (2023)
- [A review of handcrafted and deep radiomics in neurological diseases: transitioning from oncology to clinical neuroimaging](https://mlsanity.com/paper/2407.13813/) (2024)
- [Benchmarking Class Activation Map Methods for Explainable Brain Hemorrhage Classification on Hemorica Dataset](https://mlsanity.com/paper/2508.17699/) (2025)
- [Benchmarking DINOv3 for Multi-Task Stroke Analysis on Non-Contrast CT](https://mlsanity.com/paper/2509.23132/) (2025)
- [Unsupervised Acute Intracranial Hemorrhage Segmentation with Mixture Models](https://mlsanity.com/paper/2105.05891/) (2021)
- [Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT](https://mlsanity.com/paper/2211.15924/) (2022)
- [Class Activation Map-based Weakly supervised Hemorrhage Segmentation using Resnet-LSTM in Non-Contrast Computed Tomography images](https://mlsanity.com/paper/2309.16627/) (2023)
- [Hemorica: A Comprehensive CT Scan Dataset for Automated Brain Hemorrhage Classification, Segmentation, and Detection](https://mlsanity.com/paper/2509.22993/) (2025)
- [MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images](https://mlsanity.com/paper/2511.12110/) (2025)
- [A Graphical Approach For Brain Haemorrhage Segmentation](https://mlsanity.com/paper/2202.06876/) (2022)
- [Disentangled Uncertainty and Out of Distribution Detection in Medical Generative Models](https://mlsanity.com/paper/2211.06250/) (2022)
- [3D Medical Imaging Segmentation on Non-Contrast CT](https://mlsanity.com/paper/2503.08361/) (2025)
- [CT Scans As Video: Efficient Intracranial Hemorrhage Detection Using Multi-Object Tracking](https://mlsanity.com/paper/2601.02521/) (2026)
- [WeightCLIP: Aligning Datasets and Models for Weight Space Learning](https://mlsanity.com/paper/2607.03551/) (2026)
- [FSB-Net: Frequency-Spatial Boundary Network for Brain Stroke Lesion Segmentation in Non-Contrast CT](https://mlsanity.com/paper/2607.20955/) (2026)

## Abstract (arXiv)

Traumatic brain injuries could cause intracranial hemorrhage (ICH). ICH could lead to disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure. The current clinical protocol to diagnose ICH is examining Computerized Tomography (CT) scans by radiologists to detect ICH and localize its regions. However, this process relies heavily on the availability of an experienced radiologist. In this paper, we designed a study protocol to collect a dataset of 82 CT scans of subjects with traumatic brain injury. Later, the ICH regions were manually delineated in each slice by a consensus decision of two radiologists. Recently, fully convolutional networks (FCN) have shown to be successful in medical image segmentation. We developed a deep FCN, called U-Net, to segment the ICH regions from the CT scans in a fully automated manner. The method achieved a Dice coefficient of 0.31 for the ICH segmentation based on 5-fold cross-validation. The dataset is publicly available online at PhysioNet repository for future analysis and comparison.