# Unsupervised Acute Intracranial Hemorrhage Segmentation with Mixture Models

arXiv: https://arxiv.org/abs/2105.05891 · HTML: https://mlsanity.com/paper/2105.05891/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 40 references that are in the map.
- First posted on arXiv 12 May 2021.

## Builds on (most cited first, 4 of 4)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Intracranial Hemorrhage Segmentation Using Deep Convolutional Model](https://mlsanity.com/paper/1910.08643/) (2019)
- [Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans](https://mlsanity.com/paper/1803.05854/) (2018)
- [RADNET: Radiologist Level Accuracy using Deep Learning for HEMORRHAGE detection in CT Scans](https://mlsanity.com/paper/1710.04934/) (2017)

## Built on by (most cited first, 1 of 1)

- [A Graphical Approach For Brain Haemorrhage Segmentation](https://mlsanity.com/paper/2202.06876/) (2022)

## Abstract (arXiv)

Intracranial hemorrhage occurs when blood vessels rupture or leak within the brain tissue or elsewhere inside the skull. It can be caused by physical trauma or by various medical conditions and in many cases leads to death. The treatment must be started as soon as possible, and therefore the hemorrhage should be diagnosed accurately and quickly. The diagnosis is usually performed by a radiologist who analyses a Computed Tomography (CT) scan containing a large number of cross-sectional images throughout the brain. Analysing each image manually can be very time-consuming, but automated techniques can help speed up the process. While much of the recent research has focused on solving this problem by using supervised machine learning algorithms, publicly-available training data remains scarce due to privacy concerns. This problem can be alleviated by unsupervised algorithms. In this paper, we propose a fully-unsupervised algorithm which is based on the mixture models. Our algorithm utilizes the fact that the properties of hemorrhage and healthy tissues follow different distributions, and therefore an appropriate formulation of these distributions allows us to separate them through an Expectation-Maximization process. In addition, our algorithm is able to adaptively determine the number of clusters such that all the hemorrhage regions can be found without including noisy voxels. We demonstrate the results of our algorithm on publicly-available datasets that contain all different hemorrhage types in various sizes and intensities, and our results are compared to earlier unsupervised and supervised algorithms. The results show that our algorithm can outperform the other algorithms with most hemorrhage types.