From Division to Decision: Leveraging Temporal Cell-Stage Segmentation for Embryo Transferability Prediction

• URL: http://arxiv.org/abs/2605.18923v1
• Date: Mon, 18 May 2026 11:42:38 GMT
• Title: From Division to Decision: Leveraging Temporal Cell-Stage Segmentation for Embryo Transferability Prediction
• Abstract summary: Time-lapse videomicroscopy provides detailed information on early development, but is difficult to exploit because of complex motion patterns and time-consuming analysis.<n>We propose TransFACT, a transformer-based framework for modeling early developmental stages and embryo transferability.<n>Our experiments demonstrate that TransFACT, by leveraging an existing method designed for action recognition, achieves superior performance than its competitor in predicting embryo transferability.
• Score: 4.187020317965648
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate selection of bovine embryos is a challenging task, as current practice relies on a single expert assessment on the seventh day after insemination, resulting in high rates of pregnancy loss. Time-lapse videomicroscopy provides detailed information on early development, but is difficult to exploit because of complex motion patterns and time-consuming analysis. We propose TransFACT, a transformer-based framework for modeling early developmental stages and embryo transferability using 2D time-lapse videos from the first four days of development. TransFACT combines frame-level temporal features with stage-level representations, using developmental stages as auxiliary supervision to predict transferability on day four. Our experiments demonstrate that TransFACT, by leveraging an existing method designed for action recognition, achieves superior performance than its competitor in predicting embryo transferability.

Related papers

• TransVLM: A Vision-Language Framework and Benchmark for Detecting Any Shot Transitions [52.61846373082384]
  Shot Transition Detection (STD) inherently struggles with complex transitions.<n>We propose TransVLM, a Vision-Language Model (VLM) framework for STD.<n>Extensive experiments demonstrate that TransVLM achieves superior overall performance.
  arXiv  Detail & Related papers  (2026-04-30T15:05:06Z)
• Predicting Blastocyst Formation in IVF: Integrating DINOv2 and Attention-Based LSTM on Time-Lapse Embryo Images [1.948638796030649]
  A key obstacle in IVF is predicting blastocyst formation from the limited number of daily images available.<n>Many clinics lack complete time-lapse systems, so full videos are often unavailable.<n>In this study, we aimed to predict which embryos will develop into blastocysts using limited daily images from time-lapse recordings.
  arXiv  Detail & Related papers  (2026-04-14T17:49:56Z)
• Interpretation of Deep Learning Model in Embryo Selection for In Vitro Fertilization (IVF) Treatment [0.0]
  Expert embryologists conventionally grade embryos by reviewing blastocyst images to select the most optimal for transfer.<n>In this study, we introduce an explainable artificial intelligence framework for classifying embryos, employing a fusion of convolutional neural network (CNN) and long short-term memory (LSTM) architecture.<n>Our model achieves high accuracy in embryo classification while maintaining interpretability through XAI.
  arXiv  Detail & Related papers  (2025-06-07T06:25:22Z)
• Time-o1: Time-Series Forecasting Needs Transformed Label Alignment [50.54348432664401]
  Time-o1 is a transformation-augmented learning objective tailored for time-series forecasting.<n>The central idea is to transform the label sequence into decorrelated components with discriminated significance.<n>Time-o1 achieves state-of-the-art performance and is compatible with various forecast models.
  arXiv  Detail & Related papers  (2025-05-23T13:00:35Z)
• Early prediction of the transferability of bovine embryos from videomicroscopy [3.4030535409936147]
  We aim to predict the embryo transferability within four days at most, taking 2D time-lapse microscopy videos as input.<n>We propose a 3D convolutional neural network involving three pathways, which makes it multi-scale in time and able to handle appearance and motion in different ways.
  arXiv  Detail & Related papers  (2025-01-14T08:56:59Z)
• Multimodal Learning for Embryo Viability Prediction in Clinical IVF [24.257300904706902]
  In clinical In-Vitro Fertilization (IVF), identifying the most viable embryo for transfer is important to increasing the likelihood of a successful pregnancy. Traditionally, this process involves embryologists manually assessing embryos' static morphological features at specific intervals using light microscopy. This manual evaluation is not only time-intensive and costly, due to the need for expert analysis, but also inherently subjective, leading to variability in the selection process.
  arXiv  Detail & Related papers  (2024-10-21T01:58:26Z)
• LoViT: Long Video Transformer for Surgical Phase Recognition [59.06812739441785]
  We present a two-stage method, called Long Video Transformer (LoViT) for fusing short- and long-term temporal information. Our approach outperforms state-of-the-art methods on the Cholec80 and AutoLaparo datasets consistently.
  arXiv  Detail & Related papers  (2023-05-15T20:06:14Z)
• Developmental Stage Classification of EmbryosUsing Two-Stream Neural Network with Linear-Chain Conditional Random Field [74.53314729742966]
  We propose a two-stream model for developmental stage classification. Unlike previous methods, our two-stream model accepts both temporal and image information. We demonstrate our algorithm on two time-lapse embryo video datasets.
  arXiv  Detail & Related papers  (2021-07-13T19:56:01Z)
• FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset [57.30136148318641]
  Fetoscopy laser photocoagulation is a widely used procedure for the treatment of Twin-to-Twin Transfusion Syndrome (TTTS) This may lead to increased procedural time and incomplete ablation, resulting in persistent TTTS. Computer-assisted intervention may help overcome these challenges by expanding the fetoscopic field of view through video mosaicking and providing better visualization of the vessel network. We present a large-scale multi-centre dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms for the fetal environment with a focus on creating drift-free mosaics from long duration fetoscopy videos.
  arXiv  Detail & Related papers  (2021-06-10T17:14:27Z)
• Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer [57.18185972461453]
  We introduce for the first time in surgical workflow analysis Transformer to reconsider the ignored complementary effects of spatial and temporal features for accurate phase recognition. Our framework is lightweight and processes the hybrid embeddings in parallel to achieve a high inference speed.
  arXiv  Detail & Related papers  (2021-03-17T15:12:55Z)
• Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video [64.44583693846751]
  We study the semi-supervised instrument segmentation from robotic surgical videos with sparse annotations. By exploiting generated data pairs, our framework can recover and even enhance temporal consistency of training sequences. Results show that our method outperforms the state-of-the-art semisupervised methods by a large margin.
  arXiv  Detail & Related papers  (2020-07-06T02:39:32Z)
• Automated Measurements of Key Morphological Features of Human Embryos for IVF [22.121065811969473]
  Time-lapse microscopy provides clinicians with a wealth of information for selecting embryos. Here, we automate feature extraction of time-lapse microscopy of human embryos with a machine-learning pipeline of five convolutional neural networks (CNNs) Our approach greatly speeds up the measurement of quantitative, biologically relevant features that may aid in embryo selection.
  arXiv  Detail & Related papers  (2020-05-29T20:27:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.