# k-strip: A novel segmentation algorithm in k-space for the application of skull stripping

arXiv: https://arxiv.org/abs/2205.09706 · HTML: https://mlsanity.com/paper/2205.09706/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 68 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI](https://mlsanity.com/paper/1811.08839/) (2018)
- [Automated brain extraction of multi-sequence MRI using artificial neural networks](https://mlsanity.com/paper/1901.11341/) (2019)
- [The Importance of Skip Connections in Biomedical Image Segmentation](https://mlsanity.com/paper/1608.04117/) (2016)
- [Fast Training of Convolutional Networks through FFTs](https://mlsanity.com/paper/1312.5851/) (2013)
- [Spectral Representations for Convolutional Neural Networks](https://mlsanity.com/paper/1506.03767/) (2015)
- [A Survey of Complex-Valued Neural Networks](https://mlsanity.com/paper/2101.12249/) (2021)
- [Deep Learning -- A first Meta-Survey of selected Reviews across Scientific Disciplines, their Commonalities, Challenges and Research Impact](https://mlsanity.com/paper/2011.08184/) (2020)
- [GBM Volumetry using the 3D Slicer Medical Image Computing Platform](https://mlsanity.com/paper/1303.0964/) (2013)
- [Medical Deep Learning -- A systematic Meta-Review](https://mlsanity.com/paper/2010.14881/) (2020)

## Built on by (most cited first, 7 of 7)

- [Classification, Regression and Segmentation directly from k-Space in Cardiac MRI](https://mlsanity.com/paper/2407.20108/) (2024)
- [Direct Cardiac Segmentation from Undersampled K-space Using Transformers](https://mlsanity.com/paper/2406.00192/) (2024)
- [Tumor likelihood estimation on MRI prostate data by utilizing k-Space information](https://mlsanity.com/paper/2407.06165/) (2024)
- [PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation](https://mlsanity.com/paper/2504.07560/) (2025)
- [Efficient Complex-Valued Vision Transformers for MRI Classification Directly from k-Space](https://mlsanity.com/paper/2601.18392/) (2026)
- [NOIR: Neural Operator mapping for Implicit Representations](https://mlsanity.com/paper/2603.13118/) (2026)
- [Robustness of breast lesion segmentation under MRI undersampling improves with k-space-aware deep learning](https://mlsanity.com/paper/2605.22327/) (2026)

## Abstract (arXiv)

Objectives: Present a novel deep learning-based skull stripping algorithm for magnetic resonance imaging (MRI) that works directly in the information rich k-space. Materials and Methods: Using two datasets from different institutions with a total of 36,900 MRI slices, we trained a deep learning-based model to work directly with the complex raw k-space data. Skull stripping performed by HD-BET (Brain Extraction Tool) in the image domain were used as the ground truth. Results: Both datasets were very similar to the ground truth (DICE scores of 92%-98% and Hausdorff distances of under 5.5 mm). Results on slices above the eye-region reach DICE scores of up to 99%, while the accuracy drops in regions around the eyes and below, with partially blurred output. The output of k-strip often smoothed edges at the demarcation to the skull. Binary masks are created with an appropriate threshold. Conclusion: With this proof-of-concept study, we were able to show the feasibility of working in the k-space frequency domain, preserving phase information, with consistent results. Future research should be dedicated to discovering additional ways the k-space can be used for innovative image analysis and further workflows.