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Semantic Video Segmentation for Intracytoplasmic Sperm Injection Procedures

arXiv 2101.01207 ↗First posted 4 Jan 2021cs.CV cs.LGcited by 0

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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.

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.

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References · 3

  1. 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 586
  2. 2018Numerical Coordinate Regression with Convolutional Neural Networkscited by 72
  3. 2019diffGrad: An Optimization Method for Convolutional Neural Networkscited by 29

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