# SMRVIS: Point cloud extraction from 3-D ultrasound for non-destructive testing

arXiv: https://arxiv.org/abs/2306.04668 · HTML: https://mlsanity.com/paper/2306.04668/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 7 Jun 2023.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Point-E: A System for Generating 3D Point Clouds from Complex Prompts](https://mlsanity.com/paper/2212.08751/) (2022)
- [Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation](https://mlsanity.com/paper/1802.06955/) (2018)
- [Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark](https://mlsanity.com/paper/2109.14545/) (2021)
- [Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks](https://mlsanity.com/paper/2303.03667/) (2023)
- [nnU-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2011.00848/) (2020)
- [Why is the winner the best?](https://mlsanity.com/paper/2303.17719/) (2023)
- [Augmented Ultrasonic Data for Machine Learning](https://mlsanity.com/paper/1903.11399/) (2019)
- [Effect of the output activation function on the probabilities and errors in medical image segmentation](https://mlsanity.com/paper/2109.00903/) (2021)

## Abstract (arXiv)

We propose to formulate point cloud extraction from ultrasound volumes as an image segmentation problem. Through this convenient formulation, a quick prototype exploring various variants of the Residual Network, U-Net, and the Squeeze and Excitation Network was developed and evaluated. This report documents the experimental results compiled using a training dataset of five labeled ultrasound volumes and 84 unlabeled volumes that got completed in a two-week period as part of a submission to the open challenge "3D Surface Mesh Estimation for CVPR workshop on Deep Learning in Ultrasound Image Analysis". Based on external evaluation performed by the challenge's organizers, the framework came first place on the challenge's hrefhttps://www.cvpr2023-dl-ultrasound.com/Leaderboard. Source code is shared with the research community at a hrefhttps://github.com/lisatwyw/smrvispublic repository.