# Depth-Sequence Transformer (DST) for Segment-Specific ICA Calcification Mapping on Non-Contrast CT

arXiv: https://arxiv.org/abs/2507.08214 · HTML: https://mlsanity.com/paper/2507.08214/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 18 of 18)

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- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation](https://mlsanity.com/paper/2404.09556/) (2024)
- [When Attention Sink Emerges in Language Models: An Empirical View](https://mlsanity.com/paper/2410.10781/) (2024)
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- [Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study](https://mlsanity.com/paper/2304.06762/) (2023)
- [Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features](https://mlsanity.com/paper/2410.03558/) (2024)
- [Exact Expressive Power of Transformers with Padding](https://mlsanity.com/paper/2505.18948/) (2025)
- [Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement](https://mlsanity.com/paper/2007.14472/) (2020)
- [Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks](https://mlsanity.com/paper/1706.01148/) (2017)
- [H3DE-Net: Efficient and Accurate 3D Landmark Detection in Medical Imaging](https://mlsanity.com/paper/2502.14221/) (2025)
- [Diff3Dformer: Leveraging Slice Sequence Diffusion for Enhanced 3D CT Classification with Transformer Networks](https://mlsanity.com/paper/2406.17173/) (2024)
- [Automated Segmentation and Volume Measurement of Intracranial Carotid Artery Calcification on Non-Contrast CT](https://mlsanity.com/paper/2107.09442/) (2021)

## Abstract (arXiv)

While total intracranial carotid artery calcification (ICAC) volume is an established stroke biomarker, growing evidence shows this aggregate metric ignores the critical influence of plaque location, since calcification in different segments carries distinct prognostic and procedural risks. However, a finer-grained, segment-specific quantification has remained technically infeasible. Conventional 3D models are forced to process downsampled volumes or isolated patches, sacrificing the global context required to resolve anatomical ambiguity and render reliable landmark localization. To overcome this, we reformulate the 3D challenge as a Parallel Probabilistic Landmark Localization task along the 1D axial dimension. We propose the Depth-Sequence Transformer (DST), a framework that processes full-resolution CT volumes as sequences of 2D slices, learning to predict N=6 independent probability distributions that pinpoint key anatomical landmarks. Our DST framework demonstrates exceptional accuracy and robustness. Evaluated on a 100-patient clinical cohort with rigorous 5-fold cross-validation, it achieves a Mean Absolute Error (MAE) of 0.1 slices, with 96% of predictions falling within a ±1 slice tolerance. Furthermore, to validate its architectural power, the DST backbone establishes the best result on the public Clean-CC-CCII classification benchmark under an end-to-end evaluation protocol. Our work delivers the first practical tool for automated segment-specific ICAC analysis. The proposed framework provides a foundation for further studies on the role of location-specific biomarkers in diagnosis, prognosis, and procedural planning.