# Segment-and-Classify: ROI-Guided Generalizable Contrast Phase Classification in CT Using XGBoost

arXiv: https://arxiv.org/abs/2501.14066 · HTML: https://mlsanity.com/paper/2501.14066/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Builds on (most cited first, 9 of 9)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [MixConv: Mixed Depthwise Convolutional Kernels](https://mlsanity.com/paper/1907.09595/) (2019)
- [The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset](https://mlsanity.com/paper/2405.19595/) (2024)
- [Revisiting 3D ResNets for Video Recognition](https://mlsanity.com/paper/2109.01696/) (2021)
- [Phase Recognition in Contrast-Enhanced CT Scans based on Deep Learning and Random Sampling](https://mlsanity.com/paper/2203.11206/) (2022)
- [CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT](https://mlsanity.com/paper/1909.02511/) (2019)
- [Contrast Phase Classification with a Generative Adversarial Network](https://mlsanity.com/paper/1911.06395/) (2019)

## Abstract (arXiv)

Purpose: To automate contrast phase classification in CT using organ-specific features extracted from a widely used segmentation tool with a lightweight decision tree classifier. Materials and Methods: This retrospective study utilized three public CT datasets from separate institutions. The phase prediction model was trained on the WAW-TACE (median age: 66 [60,73]; 185 males) dataset, and externally validated on the VinDr-Multiphase (146 males; 63 females; 56 unk) and C4KC-KiTS (median age: 61 [50.68; 123 males) datasets. Contrast phase classification was performed using organ-specific features extracted by TotalSegmentator, followed by prediction using a gradient-boosted decision tree classifier. Results: On the VinDr-Multiphase dataset, the phase prediction model achieved the highest or comparable AUCs across all phases (>0.937), with superior F1-scores in the non-contrast (0.994), arterial (0.937), and delayed (0.718) phases. Statistical testing indicated significant performance differences only in the arterial and delayed phases (p<0.05). On the C4KC-KiTS dataset, the phase prediction model achieved the highest AUCs across all phases (>0.991), with superior F1-scores in arterial/venous (0.968) and delayed (0.935) phases. Statistical testing confirmed significant improvements over all baseline models in these two phases (p<0.05). Performance in the non-contrast class, however, was comparable across all models, with no statistically significant differences observed (p>0.05). Conclusion: The lightweight model demonstrated strong performance relative to all baseline models, and exhibited robust generalizability across datasets from different institutions.