Time-Lapse Video-Based Embryo Grading via Complementary Spatial-Temporal Pattern Mining
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910989454147584 |
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| author | Sun, Yong Wang, Yipeng Shi, Junyu Zhang, Zhiyuan Xiao, Yanmei Zhu, Lei Jiang, Manxi Nie, Qiang |
| author_facet | Sun, Yong Wang, Yipeng Shi, Junyu Zhang, Zhiyuan Xiao, Yanmei Zhu, Lei Jiang, Manxi Nie, Qiang |
| contents | Artificial intelligence has recently shown promise in automated embryo selection for In-Vitro Fertilization (IVF). However, current approaches either address partial embryo evaluation lacking holistic quality assessment or target clinical outcomes inevitably confounded by extra-embryonic factors, both limiting clinical utility. To bridge this gap, we propose a new task called Video-Based Embryo Grading - the first paradigm that directly utilizes full-length time-lapse monitoring (TLM) videos to predict embryologists' overall quality assessments. To support this task, we curate a real-world clinical dataset comprising over 2,500 TLM videos, each annotated with a grading label indicating the overall quality of embryos. Grounded in clinical decision-making principles, we propose a Complementary Spatial-Temporal Pattern Mining (CoSTeM) framework that conceptually replicates embryologists' evaluation process. The CoSTeM comprises two branches: (1) a morphological branch using a Mixture of Cross-Attentive Experts layer and a Temporal Selection Block to select discriminative local structural features, and (2) a morphokinetic branch employing a Temporal Transformer to model global developmental trajectories, synergistically integrating static and dynamic determinants for grading embryos. Extensive experimental results demonstrate the superiority of our design. This work provides a valuable methodological framework for AI-assisted embryo selection. The dataset and source code will be publicly available upon acceptance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04950 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Time-Lapse Video-Based Embryo Grading via Complementary Spatial-Temporal Pattern Mining Sun, Yong Wang, Yipeng Shi, Junyu Zhang, Zhiyuan Xiao, Yanmei Zhu, Lei Jiang, Manxi Nie, Qiang Computer Vision and Pattern Recognition Artificial intelligence has recently shown promise in automated embryo selection for In-Vitro Fertilization (IVF). However, current approaches either address partial embryo evaluation lacking holistic quality assessment or target clinical outcomes inevitably confounded by extra-embryonic factors, both limiting clinical utility. To bridge this gap, we propose a new task called Video-Based Embryo Grading - the first paradigm that directly utilizes full-length time-lapse monitoring (TLM) videos to predict embryologists' overall quality assessments. To support this task, we curate a real-world clinical dataset comprising over 2,500 TLM videos, each annotated with a grading label indicating the overall quality of embryos. Grounded in clinical decision-making principles, we propose a Complementary Spatial-Temporal Pattern Mining (CoSTeM) framework that conceptually replicates embryologists' evaluation process. The CoSTeM comprises two branches: (1) a morphological branch using a Mixture of Cross-Attentive Experts layer and a Temporal Selection Block to select discriminative local structural features, and (2) a morphokinetic branch employing a Temporal Transformer to model global developmental trajectories, synergistically integrating static and dynamic determinants for grading embryos. Extensive experimental results demonstrate the superiority of our design. This work provides a valuable methodological framework for AI-assisted embryo selection. The dataset and source code will be publicly available upon acceptance. |
| title | Time-Lapse Video-Based Embryo Grading via Complementary Spatial-Temporal Pattern Mining |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.04950 |