# Direct Cardiac Segmentation from Undersampled K-space Using Transformers

arXiv: https://arxiv.org/abs/2406.00192 · HTML: https://mlsanity.com/paper/2406.00192/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 31 May 2024.

## Builds on (most cited first, 6 of 6)

- [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021)
- [Learning a Variational Network for Reconstruction of Accelerated MRI Data](https://mlsanity.com/paper/1704.00447/) (2017)
- [Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences](https://mlsanity.com/paper/1806.04066/) (2018)
- [3D Consistent & Robust Segmentation of Cardiac Images by Deep Learning with Spatial Propagation](https://mlsanity.com/paper/1804.09400/) (2018)
- [Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modeling](https://mlsanity.com/paper/2307.12672/) (2023)
- [k-strip: A novel segmentation algorithm in k-space for the application of skull stripping](https://mlsanity.com/paper/2205.09706/) (2022)

## Built on by (most cited first, 5 of 5)

- [Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond](https://mlsanity.com/paper/2504.13037/) (2025)
- [Classification, Regression and Segmentation directly from k-Space in Cardiac MRI](https://mlsanity.com/paper/2407.20108/) (2024)
- [Deep Learning Assisted Outer Volume Removal for Highly-Accelerated Real-Time Dynamic MRI](https://mlsanity.com/paper/2505.00643/) (2025)
- [No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space](https://mlsanity.com/paper/2603.09945/) (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)

The prevailing deep learning-based methods of predicting cardiac segmentation involve reconstructed magnetic resonance (MR) images. The heavy dependency of segmentation approaches on image quality significantly limits the acceleration rate in fast MR reconstruction. Moreover, the practice of treating reconstruction and segmentation as separate sequential processes leads to artifact generation and information loss in the intermediate stage. These issues pose a great risk to achieving high-quality outcomes. To leverage the redundant k-space information overlooked in this dual-step pipeline, we introduce a novel approach to directly deriving segmentations from sparse k-space samples using a transformer (DiSK). DiSK operates by globally extracting latent features from 2D+time k-space data with attention blocks and subsequently predicting the segmentation label of query points. We evaluate our model under various acceleration factors (ranging from 4 to 64) and compare against two image-based segmentation baselines. Our model consistently outperforms the baselines in Dice and Hausdorff distances across foreground classes for all presented sampling rates.