# Automatic Volumetric Segmentation of Additive Manufacturing Defects with 3D U-Net

arXiv: https://arxiv.org/abs/2101.08993 · HTML: https://mlsanity.com/paper/2101.08993/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 12 of 12)

- [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)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Superhuman Accuracy on the SNEMI3D Connectomics Challenge](https://mlsanity.com/paper/1706.00120/) (2017)
- [Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning](https://mlsanity.com/paper/1808.02518/) (2018)
- [Adapting Mask-RCNN for Automatic Nucleus Segmentation](https://mlsanity.com/paper/1805.00500/) (2018)
- [Region-based semantic segmentation with end-to-end training](https://mlsanity.com/paper/1607.07671/) (2016)

## Built on by (most cited first, 4 of 4)

- [Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey](https://mlsanity.com/paper/2211.05565/) (2022)
- [Adapting Segment Anything Model (SAM) to Experimental Datasets via Fine-Tuning on GAN-based Simulation: A Case Study in Additive Manufacturing](https://mlsanity.com/paper/2412.11381/) (2024)
- [Synthetic-to-real Composite Semantic Segmentation in Additive Manufacturing](https://mlsanity.com/paper/2210.07466/) (2022)
- [An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anything](https://mlsanity.com/paper/2312.04063/) (2023)

## Abstract (arXiv)

Segmentation of additive manufacturing (AM) defects in X-ray Computed Tomography (XCT) images is challenging, due to the poor contrast, small sizes and variation in appearance of defects. Automatic segmentation can, however, provide quality control for additive manufacturing. Over recent years, three-dimensional convolutional neural networks (3D CNNs) have performed well in the volumetric segmentation of medical images. In this work, we leverage techniques from the medical imaging domain and propose training a 3D U-Net model to automatically segment defects in XCT images of AM samples. This work not only contributes to the use of machine learning for AM defect detection but also demonstrates for the first time 3D volumetric segmentation in AM. We train and test with three variants of the 3D U-Net on an AM dataset, achieving a mean intersection of union (IOU) value of 88.4%.