# Semantic Video Segmentation for Intracytoplasmic Sperm Injection Procedures

arXiv: https://arxiv.org/abs/2101.01207 · HTML: https://mlsanity.com/paper/2101.01207/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 4 Jan 2021.

## Builds on (most cited first, 3 of 3)

- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Numerical Coordinate Regression with Convolutional Neural Networks](https://mlsanity.com/paper/1801.07372/) (2018)
- [diffGrad: An Optimization Method for Convolutional Neural Networks](https://mlsanity.com/paper/1909.11015/) (2019)

## Abstract (arXiv)

We present the first deep learning model for the analysis of intracytoplasmic sperm injection (ICSI) procedures. Using a dataset of ICSI procedure videos, we train a deep neural network to segment key objects in the videos achieving a mean IoU of 0.962, and to localize the needle tip achieving a mean pixel error of 3.793 pixels at 14 FPS on a single GPU. We further analyze the variation between the dataset's human annotators and find the model's performance to be comparable to human experts.