# Learning-Based Patch-Wise Metal Segmentation with Consistency Check

arXiv: https://arxiv.org/abs/2101.10914 · HTML: https://mlsanity.com/paper/2101.10914/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 26 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images](https://mlsanity.com/paper/2009.07469/) (2020)

## Built on by (most cited first, 2 of 2)

- [Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Future](https://mlsanity.com/paper/2108.04543/) (2021)
- [View-Consistent Metal Segmentation in the Projection Domain for Metal Artifact Reduction in CBCT -- An Investigation of Potential Improvement](https://mlsanity.com/paper/2112.02101/) (2021)

## Abstract (arXiv)

Metal implants that are inserted into the patient's body during trauma interventions cause heavy artifacts in 3D X-ray acquisitions. Metal Artifact Reduction (MAR) methods, whose first step is always a segmentation of the present metal objects, try to remove these artifacts. Thereby, the segmentation is a crucial task which has strong influence on the MAR's outcome. This study proposes and evaluates a learning-based patch-wise segmentation network and a newly proposed Consistency Check as post-processing step. The combination of the learned segmentation and Consistency Check reaches a high segmentation performance with an average IoU score of 0.924 on the test set. Furthermore, the Consistency Check proves the ability to significantly reduce false positive segmentations whilst simultaneously ensuring consistent segmentations.