# CT Perfusion is All We Need: 4D CNN Segmentation of Penumbra and Core in Patients With Suspected Ischemic Stroke

arXiv: https://arxiv.org/abs/2303.08757 · HTML: https://mlsanity.com/paper/2303.08757/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 83 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation](https://mlsanity.com/paper/1810.07842/) (2018)
- [3D Deep Learning on Medical Images: A Review](https://mlsanity.com/paper/2004.00218/) (2020)
- [Spatially-sparse convolutional neural networks](https://mlsanity.com/paper/1409.6070/) (2014)
- [Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning](https://mlsanity.com/paper/1812.02496/) (2018)
- [Ischemic Stroke Lesion Segmentation in CT Perfusion Scans using Pyramid Pooling and Focal Loss](https://mlsanity.com/paper/1811.01085/) (2018)
- [4D CNN for semantic segmentation of cardiac volumetric sequences](https://mlsanity.com/paper/1906.07295/) (2019)
- [Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection](https://mlsanity.com/paper/2203.10039/) (2022)
- [Activity Recognition From Newborn Resuscitation Videos](https://mlsanity.com/paper/2303.07789/) (2023)
- [Differentiable Deconvolution for Improved Stroke Perfusion Analysis](https://mlsanity.com/paper/2103.17111/) (2021)
- [Deep learning with 4D spatio-temporal data representations for OCT-based force estimation](https://mlsanity.com/paper/2005.10033/) (2020)
- [CNN Based Segmentation of Infarcted Regions in Acute Cerebral Stroke Patients From Computed Tomography Perfusion Imaging](https://mlsanity.com/paper/2104.03002/) (2021)
- [Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation](https://mlsanity.com/paper/2303.01332/) (2023)
- [4D Spatio-Temporal Convolutional Networks for Object Position Estimation in OCT Volumes](https://mlsanity.com/paper/2007.01044/) (2020)

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

- [Bi-temporal Image-driven Acute Stroke Evolution Analysis](https://mlsanity.com/paper/2602.07535/) (2026)

## Abstract (arXiv)

Precise and fast prediction methods for ischemic areas comprised of dead tissue, core, and salvageable tissue, penumbra, in acute ischemic stroke (AIS) patients are of significant clinical interest. They play an essential role in improving diagnosis and treatment planning. Computed Tomography (CT) scan is one of the primary modalities for early assessment in patients with suspected AIS. CT Perfusion (CTP) is often used as a primary assessment to determine stroke location, severity, and volume of ischemic lesions. Current automatic segmentation methods for CTP mostly use already processed 3D parametric maps conventionally used for clinical interpretation by radiologists as input. Alternatively, the raw CTP data is used on a slice-by-slice basis as 2D+time input, where the spatial information over the volume is ignored. In addition, these methods are only interested in segmenting core regions, while predicting penumbra can be essential for treatment planning. This paper investigates different methods to utilize the entire 4D CTP as input to fully exploit the spatio-temporal information, leading us to propose a novel 4D convolution layer. Our comprehensive experiments on a local dataset of 152 patients divided into three groups show that our proposed models generate more precise results than other methods explored. Adopting the proposed 4D mJ-Net, a Dice Coefficient of 0.53 and 0.23 is achieved for segmenting penumbra and core areas, respectively. The code is available on https://github.com/Biomedical-Data-Analysis-Laboratory/4D-mJ-Net.git.