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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:1908.09637 (eess)
[Submitted on 22 Aug 2019]

Title:Multi-Task Deep Learning with Dynamic Programming for Embryo Early Development Stage Classification from Time-Lapse Videos

Authors:Zihan Liu, Bo Huang, Yuqi Cui, Yifan Xu, Bo Zhang, Lixia Zhu, Yang Wang, Lei Jin, Dongrui Wu
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Abstract:Time-lapse is a technology used to record the development of embryos during in-vitro fertilization (IVF). Accurate classification of embryo early development stages can provide embryologists valuable information for assessing the embryo quality, and hence is critical to the success of IVF. This paper proposes a multi-task deep learning with dynamic programming (MTDL-DP) approach for this purpose. It first uses MTDL to pre-classify each frame in the time-lapse video to an embryo development stage, and then DP to optimize the stage sequence so that the stage number is monotonically non-decreasing, which usually holds in practice. Different MTDL frameworks, e.g., one-to-many, many-to-one, and many-to-many, are investigated. It is shown that the one-to-many MTDL framework achieved the best compromise between performance and computational cost. To our knowledge, this is the first study that applies MTDL to embryo early development stage classification from time-lapse videos.
Subjects: Image and Video Processing (eess.IV); Computers and Society (cs.CY); Machine Learning (cs.LG)
Cite as: arXiv:1908.09637 [eess.IV]
  (or arXiv:1908.09637v1 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.1908.09637

Submission history

From: Dongrui Wu [view email]
[v1] Thu, 22 Aug 2019 22:18:25 UTC (980 KB)
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