# Zero-Shot Domain Adaptation in CT Segmentation by Filtered Back Projection Augmentation

arXiv: https://arxiv.org/abs/2107.08543 · HTML: https://mlsanity.com/paper/2107.08543/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 18 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [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)
- [Confounding variables can degrade generalization performance of radiological deep learning models](https://mlsanity.com/paper/1807.00431/) (2018)
- [CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification](https://mlsanity.com/paper/2006.01441/) (2020)
- [Practical Window Setting Optimization for Medical Image Deep Learning](https://mlsanity.com/paper/1812.00572/) (2018)

## Built on by (most cited first, 3 of 3)

- [Adaptation to CT Reconstruction Kernels by Enforcing Cross-domain Feature Maps Consistency](https://mlsanity.com/paper/2203.14616/) (2022)
- [Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation](https://mlsanity.com/paper/2211.00303/) (2022)
- [M3DA: Benchmark for Unsupervised Domain Adaptation in 3D Medical Image Segmentation](https://mlsanity.com/paper/2502.17029/) (2025)

## Abstract (arXiv)

Domain shift is one of the most salient challenges in medical computer vision. Due to immense variability in scanners' parameters and imaging protocols, even images obtained from the same person and the same scanner could differ significantly. We address variability in computed tomography (CT) images caused by different convolution kernels used in the reconstruction process, the critical domain shift factor in CT. The choice of a convolution kernel affects pixels' granularity, image smoothness, and noise level. We analyze a dataset of paired CT images, where smooth and sharp images were reconstructed from the same sinograms with different kernels, thus providing identical anatomy but different style. Though identical predictions are desired, we show that the consistency, measured as the average Dice between predictions on pairs, is just 0.54. We propose Filtered Back-Projection Augmentation (FBPAug), a simple and surprisingly efficient approach to augment CT images in sinogram space emulating reconstruction with different kernels. We apply the proposed method in a zero-shot domain adaptation setup and show that the consistency boosts from 0.54 to 0.92 outperforming other augmentation approaches. Neither specific preparation of source domain data nor target domain data is required, so our publicly released FBPAug can be used as a plug-and-play module for zero-shot domain adaptation in any CT-based task.