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.
@article{arxiv.2101.01207,
title = {Semantic Video Segmentation for Intracytoplasmic Sperm Injection Procedures},
author = {Chloe He and Raksha Jain and Jérôme Chambost and Céline Jacques and Cristina Hickman},
journal= {arXiv preprint arXiv:2101.01207},
year = {2023}
}
Comments
Accepted at the 'Medical Imaging meets NeurIPS Workshop' at the 34th Conference on Neural Information Processing Systems