AI-Powered Embryo Selection for IVF

Abstract

In vitro fertilization procedures require accurate and timely embryo assessment to optimize clinical outcomes. This article presents a comprehensive deep learning-based system for automated embryo evaluation from time-lapse microscopy videos. The proposed framework integrates multiple specialized neural network architectures operating on both spatial and temporal modalities: a U-Net-ResNet hybrid for embryo segmentation, multi-task convolutional networks for blastocyst detection and morphological grading, a Video Swin Transformer with three-dimensional attention mechanisms for developmental milestone prediction, and a hybrid Siamese-CNN architecture for morphokinetic event detection. The system addresses key clinical prediction tasks including developmental stage classification, morphological quality assessment, euploidy prediction, and pregnancy outcome estimation. Trained on multi-center datasets from commercial time-lapse imaging systems, the framework achieves competitive performance metrics for binary classification tasks, strong segmentation overlap measures, and balanced accuracy across multi-class grading tasks. The production deployment utilizes a model serving framework with optimized models, enabling real-time inference with sub-second latency for single-frame analysis and efficient batch processing for video sequences.

1. Introduction

1.1 Problem Statement and Clinical Motivation

In vitro fertilization represents a critical reproductive technology where embryo selection significantly impacts clinical success rates. Traditional embryo assessment relies on manual morphological evaluation by trained embryologists, who examine static images or time-lapse videos to identify developmental milestones and grade embryo quality. This process exhibits inherent inter-observer variability, requires specialized expertise, and operates under time constraints that limit comprehensive analysis of entire developmental trajectories spanning multiple days of culture.

The advent of time-lapse imaging systems, including commercial time-lapse imaging platforms, generates continuous video streams capturing embryo development from fertilization through blastocyst formation. These systems produce thousands of frames per embryo, creating opportunities for detailed quantitative analysis but simultaneously overwhelming manual evaluation capacity. Moreover, subtle morphokinetic patterns predictive of developmental potential and chromosomal abnormalities may escape human detection due to perceptual limitations and cognitive biases.

1.2 Contributions

This work presents an integrated artificial intelligence platform addressing multiple facets of automated embryo assessment:

Multi-scale spatial analysis: Combining segmentation networks operating at pixel-level precision with classification architectures extracting global morphological features to enable both localized embryo detection and holistic quality evaluation.

Temporal reasoning over developmental trajectories: Employing three-dimensional attention mechanisms within transformer architectures to capture long-range temporal dependencies spanning hours to days of embryo culture, facilitating prediction of future developmental outcomes from early timepoint observations.

Hierarchical task decomposition: Structuring the assessment pipeline as a sequence of specialized models handling embryo localization, validity filtering, stage classification, morphological grading, and clinical outcome prediction, allowing independent optimization of each component while maintaining end-to-end differentiability where beneficial.

Cross-device generalization: Training on heterogeneous datasets from multiple time-lapse imaging platforms with varying optical characteristics, spatial resolutions, and temporal sampling rates to ensure robust performance across clinical deployment contexts.

Production-ready deployment architecture: Implementing model serving infrastructure with hardware-accelerated inference, request batching, caching mechanisms, and standardized API interfaces suitable for integration into clinical workflow management systems.

1.3 Positioning Relative to Prior Work

The system builds upon several foundational developments in medical image analysis and video understanding. The approach draws inspiration from successful applications of fully convolutional networks to biomedical image segmentation while extending these methods to handle the unique challenges of embryo imaging, including variable positioning, inconsistent illumination, and partial occlusion. The multi-task learning framework for quality assessment follows principles established in transfer learning for medical imaging, leveraging ImageNet-pretrained feature extractors adapted to embryological morphology through progressive fine-tuning strategies.

The temporal modeling component advances beyond simple frame aggregation methods by incorporating recent innovations in video transformers originally developed for action recognition. However, the adaptation to embryo analysis requires addressing substantially longer temporal spans with irregular sampling, motivating the design of specialized temporal encoding mechanisms and attention window configurations tailored to developmental biology timescales.

2. Related Work

2.1 Medical Image Segmentation

U-Net architectures have established themselves as the dominant paradigm for biomedical image segmentation tasks [1]. The encoder-decoder structure with skip connections enables precise localization while maintaining contextual information across scales [2]. Extensions incorporating residual connections and batch normalization have further improved convergence properties and generalization performance [3]. For embryo segmentation specifically, the challenge lies in handling low-contrast boundaries and distinguishing the embryo from debris or zona pellucida structures, requiring careful loss function design and data augmentation strategies [4][5].

2.2 Convolutional Neural Networks for Medical Classification

Deep residual networks introduced skip connections that alleviate gradient vanishing in very deep architectures, enabling training of models exceeding one hundred layers [6]. DenseNet architectures extended this concept with dense connectivity patterns that maximize information flow between layers [7]. MobileNet variants optimize for computational efficiency through depthwise separable convolutions, enabling deployment on resource-constrained devices [8]. Transfer learning from ImageNet representations has proven effective for medical imaging despite domain shift, particularly when combined with progressive unfreezing strategies [9][10].

For embryo quality assessment, these architectures must be adapted to handle grayscale microscopy images with distinct morphological patterns compared to natural images [11]. Multi-task learning frameworks that jointly predict multiple quality indicators have shown improvements over single-task approaches by enabling shared representation learning [12][13].

2.3 Ordinal Regression and Ranking

Embryo grading inherently constitutes an ordinal classification problem where class labels possess natural ordering. Standard cross-entropy loss functions fail to encode this structure, motivating development of ordinal regression techniques [14]. Approaches include cumulative link models, threshold-based formulations, and ranking losses that explicitly penalize rank violations [15][16]. For embryo grading, maintaining ordinality proves particularly important as adjacent grade categories possess significantly different clinical implications [17].

2.4 Video Understanding and Temporal Modeling

Convolutional approaches to video analysis initially extended two-dimensional convolutions to three dimensions, enabling joint spatiotemporal feature learning [18]. Two-stream architectures separately process RGB frames and optical flow to capture appearance and motion cues [19]. Temporal segment networks aggregate predictions across sparsely sampled frames to handle long videos [20].

Recent transformer-based architectures have achieved state-of-the-art results on video recognition benchmarks [21]. Video Swin Transformer specifically introduces hierarchical spatiotemporal attention with shifted windows to efficiently process long sequences [22]. These architectures excel at capturing long-range dependencies while maintaining computational tractability through factorized attention mechanisms.

2.5 Metric Learning and Siamese Networks

Siamese architectures learn embedding spaces where semantically similar inputs map to proximate representations [23]. Contrastive losses and triplet losses encourage embeddings to cluster by class while maintaining separation between classes [24][25]. For temporal event detection, Siamese networks can compare frames to identify morphokinetic transitions by measuring embedding distance [26].

2.6 Embryo Assessment Automation

Early computational approaches to embryo evaluation employed hand-crafted features describing morphology and texture [27]. Statistical models incorporating morphokinetic timing parameters showed correlations with implantation outcomes [28]. Recent deep learning efforts have demonstrated feasibility of automated blastocyst grading and quality prediction [29][30]. However, most prior systems address isolated prediction tasks rather than comprehensive end-to-end assessment pipelines spanning segmentation through outcome prediction.

3. Method

3.1 System Architecture Overview

The proposed framework implements a hierarchical processing pipeline operating on time-lapse embryo videos. Given an input video represented as a sequence of grayscale frames denoted by $\mathbf{V} = \{\mathbf{I}_1, \mathbf{I}_2, ..., \mathbf{I}_T\}$ where $\mathbf{I}_t \in \mathbb{R}^{H \times W}$ represents the frame at time $t$ , the system produces comprehensive assessments including segmentation masks, validity classifications, developmental stage predictions, morphological grades, and clinical outcome probabilities.

The pipeline consists of five primary components executed sequentially:

Segmentation Module: Processes individual frames to generate binary masks localizing the embryo region and computing spatial features including centroid coordinates and bounding box dimensions.

Validity Filter: Classifies frames as suitable or unsuitable for analysis based on focus quality, illumination adequacy, and presence of artifacts.

Blastocyst Detection: Identifies frames where the embryo has reached blastocyst developmental stage, characterized by blastocoele cavity formation and cellular differentiation.

Morphological Grading: Assigns quality scores to blastocyst-stage embryos across multiple dimensions including inner cell mass quality, trophectoderm quality, and expansion grade.

Temporal Assessment: Analyzes video sequences to predict early developmental milestones, chromosomal euploidy status, and pregnancy outcome likelihood.

3.2 Embryo Segmentation Network

The segmentation module employs a modified U-Net architecture with a ResNet encoder backbone to generate pixel-wise embryo localization masks.

3.2.1 Architecture Design

The encoder pathway consists of four downsampling stages using standard convolutional blocks with batch normalization and ReLU activations. Each stage progressively reduces spatial resolution through max pooling while increasing filter dimensions, extracting hierarchical feature representations at multiple scales.

The bottleneck layer at maximum depth processes feature representations at one-sixteenth spatial resolution relative to input dimensions, applying dropout regularization with probability 0.5 at the deepest encoder stage and bottleneck layer to prevent overfitting given limited training data.

The decoder pathway mirrors the encoder structure with four upsampling stages. Each decoder stage concatenates upsampled features from the previous stage with corresponding encoder features via skip connections:

\mathbf{y}^{(l)} = \text{Conv}(\text{Concat}(\text{Upsample}(\mathbf{y}^{(l+1)}), \mathbf{x}^{(L-l)}))

where $L$ denotes total depth and $\text{Upsample}$ performs bilinear interpolation followed by convolution to refine spatial details.

The final layer applies a one-by-one convolution with sigmoid activation to produce probability maps $\mathbf{P} \in [0,1]^{H \times W}$ representing pixel-wise embryo presence likelihood.

3.2.2 Loss Function

Training optimizes a weighted combination of Dice loss and binary cross-entropy. The Dice coefficient measures overlap between predicted mask $\hat{\mathbf{M}}$ and ground truth $\mathbf{M}$ :

\text{Dice}(\mathbf{M}, \hat{\mathbf{M}}) = \frac{2\sum_{i,j} M_{ij}\hat{M}_{ij} + \epsilon}{\sum_{i,j} M_{ij} + \sum_{i,j} \hat{M}_{ij} + \epsilon}

where $\epsilon = 1$ provides smoothing to handle empty masks. The Dice loss equals one minus this coefficient:

\mathcal{L}_{\text{Dice}} = 1 - \text{Dice}(\mathbf{M}, \hat{\mathbf{M}})

For challenging cases with boundary ambiguity, a weighted square error loss applies increased penalties based on mask intensity values. The weight map is computed as:

W_{ij} = \alpha \cdot M_{ij} + \beta

with $\alpha = 100$ and $\beta = 10$ determined empirically, where weights are proportional to the ground truth mask values rather than distance transforms. The weighted square error loss becomes:

\mathcal{L}_{\text{WSE}} = \frac{1}{HW}\sum_{i,j} W_{ij}(M_{ij} - \hat{M}_{ij})^2

The total segmentation loss combines these terms:

\mathcal{L}_{\text{seg}} = \mathcal{L}_{\text{Dice}} + \lambda_{\text{WSE}} \mathcal{L}_{\text{WSE}}

where $\lambda_{\text{WSE}}$ balances contributions.

3.2.3 Postprocessing

Binary masks undergo morphological filtering to remove small disconnected components and fill interior holes. Connected component analysis identifies the largest contiguous region, from which centroid coordinates $(c_x, c_y)$ are computed via spatial moment calculation:

c_x = \frac{\sum_{i,j} j \cdot M_{ij}}{\sum_{i,j} M_{ij}}, \quad c_y = \frac{\sum_{i,j} i \cdot M_{ij}}{\sum_{i,j} M_{ij}}

These centroids guide subsequent cropping operations that extract fixed-size embryo-centered regions for downstream classification tasks.

3.3 Multi-Task Classification for Blastocyst Assessment

Blastocyst detection and quality grading employ transfer learning with ImageNet-pretrained convolutional architectures adapted through multi-task learning.

3.3.1 Base Architecture and Transfer Learning

The system primarily employs ResNet-50 as the backbone architecture, selected for its balance of representational capacity and computational efficiency. For ResNet-50, the architecture comprises five stages with progressively increasing channel dimensions. Given an input image $\mathbf{I} \in \mathbb{R}^{H \times W}$ , the backbone extracts high-level features:

\mathbf{f} = \text{GlobalAvgPool}(\text{ResNet}(\mathbf{I})) \in \mathbb{R}^{2048}

where Global Average Pooling aggregates spatial dimensions to produce a fixed-length feature vector.

Transfer learning initializes convolutional weights from ImageNet pretraining. Progressive fine-tuning first freezes the backbone while training only task-specific heads, then gradually unfreezes deeper layers. This strategy prevents catastrophic forgetting while adapting representations to embryological morphology.

3.3.2 Multi-Task Learning Framework

The system implements separate prediction heads for multiple correlated tasks. Each head consists of fully connected layers with batch normalization and dropout regularization. For binary blastocyst detection, a classification head produces logits:

z_{\text{blasto}} = \mathbf{W}_{\text{blasto}}^T \mathbf{h} + b_{\text{blasto}}

where $\mathbf{h} = \text{Dropout}(\text{BN}(\text{Dense}(\mathbf{f})))$ represents intermediate task-specific features.

For morphological grading, the system predicts multiple quality dimensions including inner cell mass grade, trophectoderm grade, and expansion level. Each dimension utilizes an ordinal regression formulation.

3.3.3 Ordinal Regression for Quality Grading

Morphological grades possess natural ordering, for example inner cell mass quality ranks as A (excellent), B (good), C (fair), or D (poor). To encode this structure, the system employs an ordinal regression approach where a continuous latent variable $z \in \mathbb{R}$ maps to discrete grades through learned thresholds.

The production system trains the network to predict a continuous score in the interval zero to one, then applies post-processing normalization and binning. Let $s$ denote the raw network output. Normalization scales predictions to span the observed training set range:

s_{\text{norm}} = \frac{s - s_{\min}}{s_{\max} - s_{\min}}

where $s_{\min}$ and $s_{\max}$ correspond to the 2.5th and 97.5th percentiles of training predictions, providing robustness to outliers.

Discrete grades are assigned by comparing normalized scores against quantile-based thresholds derived from training label distributions. If training examples exhibit proportions $p_1, p_2, ..., p_K$ across $K$ grades, thresholds are set such that:

\tau_k = \text{Quantile}(s_{\text{norm}}, \sum_{i=1}^{k} p_i)

This approach ensures predicted grade distributions match training label distributions, accounting for class imbalance.

3.3.4 Multi-Task Loss Function

The total loss combines binary cross-entropy for blastocyst detection and mean squared error for ordinal grade prediction:

\mathcal{L}_{\text{multi}} = \mathcal{L}_{\text{BCE}}(y_{\text{blasto}}, \hat{y}_{\text{blasto}}) + \sum_{g \in \text{grades}} \mathcal{L}_{\text{MSE}}(s_g, \hat{s}_g)

where the summation ranges over multiple grading dimensions. Shared backbone representations enable the network to learn features beneficial across related tasks, improving data efficiency and generalization.

3.4 Video Swin Transformer for Temporal Analysis

Prediction of developmental milestones and clinical outcomes requires analyzing temporal patterns across extended video sequences spanning hours to days. The system employs a Video Swin Transformer architecture that extends spatial attention mechanisms to the spatiotemporal domain.

3.4.1 Three-Dimensional Patch Embedding

Video input undergoes three-dimensional patch partitioning that groups spatiotemporal neighborhoods into tokens. Given a video clip $\mathbf{V} \in \mathbb{R}^{T \times H \times W \times C}$ with temporal depth $T$ , spatial dimensions $H \times W$ , and channel count $C$ , patches of size $P_T \times P_H \times P_W$ are extracted and linearly projected:

\mathbf{E} = \text{Linear}(\text{Reshape}(\mathbf{V})) \in \mathbb{R}^{(T/P_T \times H/P_H \times W/P_W) \times D}

where $D$ denotes embedding dimension. Typical configurations use patch sizes of $(2, 4, 4)$ to balance spatial and temporal granularity.

3.4.2 Hierarchical Spatiotemporal Attention

The architecture organizes into multiple stages with progressively decreasing spatiotemporal resolution and increasing channel dimensions, analogous to convolutional neural network hierarchies. Each stage contains several transformer blocks implementing windowed multi-head self-attention.

Within each block, the feature tensor is partitioned into non-overlapping three-dimensional windows of size $W_T \times W_H \times W_W$ . Self-attention operates independently within each window, computing attention scores between all token pairs within the same window. For a window containing $M = W_T \cdot W_H \cdot W_W$ tokens with feature representations $\mathbf{X} \in \mathbb{R}^{M \times D}$ , query, key, and value projections are computed:

\mathbf{Q} = \mathbf{X}\mathbf{W}_Q, \quad \mathbf{K} = \mathbf{X}\mathbf{W}_K, \quad \mathbf{V} = \mathbf{X}\mathbf{W}_V

Multi-head attention computes:

\text{Attention}(\mathbf{Q}, \mathbf{K}, \mathbf{V}) = \text{softmax}\left(\frac{\mathbf{Q}\mathbf{K}^T}{\sqrt{d_k}} + \mathbf{B}\right)\mathbf{V}

where $d_k = D / h$ for $h$ attention heads, and $\mathbf{B}$ represents a learnable relative position bias that encodes spatiotemporal relationships between tokens within the window.

3.4.3 Shifted Window Attention

To enable information exchange across windows while maintaining computational efficiency, successive transformer blocks alternate between regular windowing and shifted windowing schemes. In shifted configurations, window partitioning boundaries are displaced by $(\lfloor W_T/2 \rfloor, \lfloor W_H/2 \rfloor, \lfloor W_W/2 \rfloor)$ , creating connections between previously isolated windows. This mechanism facilitates hierarchical modeling of dependencies spanning the entire spatiotemporal volume.

3.4.4 Temporal Sampling Strategy

Embryo videos contain thousands of frames spanning multiple days of development, exceeding GPU memory capacity for dense sampling. The system employs intelligent temporal sampling strategies that select informative frames while maintaining temporal context.

For developmental milestone prediction around day three post-fertilization, the sampling strategy identifies a reference timepoint and extracts frames at regular intervals in a surrounding temporal window. Given a desired clip length of $T$ frames and a sampling rate of $r$ frames per selected frame, the system samples frames at positions:

t_i = t_{\text{ref}} + (i - T/2) \cdot r, \quad i \in \{0, 1, ..., T-1\}

where $t_{\text{ref}}$ corresponds to the evaluation timepoint.

For longer-term prediction tasks such as euploidy assessment, a non-linear sampling strategy densely samples early developmental stages while sparsely sampling later stages, reflecting the biological insight that early cleavage timing patterns correlate strongly with chromosomal normality.

3.4.5 Patch Merging for Downsampling

Between stages, patch merging layers reduce spatiotemporal resolution while expanding channel dimension. Adjacent patches along each dimension are concatenated, and a linear layer projects to increased channel count:

\mathbf{X}^{(l+1)} = \text{Linear}(\text{Concat}(\mathbf{X}^{(l)}))

This hierarchical structure enables the network to capture both fine-grained local patterns and coarse global context.

3.4.6 Classification Heads

After processing through all stages, global average pooling aggregates the spatiotemporal feature volume into a fixed-length vector:

\mathbf{z} = \text{GlobalAvgPool}(\mathbf{X}^{(L)}) \in \mathbb{R}^{D_L}

where $L$ indexes the final stage. Task-specific classification heads implemented as multi-layer perceptrons produce predictions for multiple developmental outcomes. For Day 3 embryo assessment, the system predicts two binary targets: high-quality full blastocyst development likelihood and poor-quality arrest likelihood.

The multi-task loss combines binary cross-entropy terms:

\mathcal{L}_{\text{day3}} = \mathcal{L}_{\text{BCE}}(y_{\text{FHB}}, \hat{y}_{\text{FHB}}) + \mathcal{L}_{\text{BCE}}(y_{\text{avoid}}, \hat{y}_{\text{avoid}})

For euploidy prediction, a similar formulation estimates chromosomal normality probability from video sequences preceding preimplantation genetic testing.

3.5 Morphokinetic Event Detection via Siamese Networks

Precise identification of developmental events including pronuclear fading, cleavage divisions, and compaction enables construction of morphokinetic timelines with prognostic value. The system employs a Siamese network architecture that learns frame embeddings where temporal proximity to specific events corresponds to embedding space distance.

3.5.1 Siamese Architecture Design

The network consists of a shared feature extraction backbone followed by an embedding projection head. The backbone utilizes a ResNet-18 architecture truncated after layer1 (the first group of residual blocks) to operate at relatively high spatial resolution, preserving fine morphological details relevant to event detection.

For a given frame $\mathbf{I}_t$ , the embedding function produces a low-dimensional representation:

\mathbf{e}_t = f_{\theta}(\mathbf{I}_t) \in \mathbb{R}^{d}

where $f_{\theta}$ denotes the parameterized embedding network with typical embedding dimension $d = 16$ .

3.5.2 Contrastive Learning Framework

The network is trained using pairs of frames with known temporal relationships relative to target events. Positive pairs consist of frames both near the same event occurrence, while negative pairs involve frames far from each other temporally or associated with different events.

The contrastive loss encourages small distances for positive pairs and large distances for negative pairs:

\mathcal{L}_{\text{contrast}} = \sum_{\text{pos}} \|\mathbf{e}_i - \mathbf{e}_j\|^2 + \sum_{\text{neg}} \max(0, m - \|\mathbf{e}_i - \mathbf{e}_j\|)^2

where $m$ represents a margin hyperparameter defining the minimum desired separation for negative pairs.

3.5.3 Event Detection via Embedding Distance

At inference time, event detection proceeds by computing embeddings for all frames in a video sequence, then analyzing the temporal profile of distances to reference embeddings representing canonical event frames. A trained reference embedding $\mathbf{e}_{\text{ref}}^{(k)}$ for event type $k$ is established during training.

For each frame $t$ , the distance to the reference embedding is computed:

d_t^{(k)} = \|\mathbf{e}_t - \mathbf{e}_{\text{ref}}^{(k)}\|

Events are detected as local minima in the distance profile, where a frame exhibits smaller distance than surrounding frames within a temporal window. Additional filtering based on distance thresholds removes spurious detections.

3.6 Clinical Outcome Prediction

The framework includes models predicting clinical endpoints including pregnancy occurrence and embryo viability. These models operate on single frames selected based on morphological quality scores from earlier pipeline stages.

3.6.1 Multi-Frame Aggregation Architecture

For outcomes requiring integration of information from multiple timepoints, the system employs a two-stream architecture processing two frames separated by a fixed temporal interval. Separate backbone networks extract features from each frame:

\mathbf{f}_1 = \text{Backbone}(\mathbf{I}_1), \quad \mathbf{f}_2 = \text{Backbone}(\mathbf{I}_2)

Features are concatenated and processed through task-specific prediction heads:

\mathbf{h} = \text{MLP}(\text{Concat}(\mathbf{f}_1, \mathbf{f}_2))

\hat{y}_{\text{preg}} = \sigma(\mathbf{W}_{\text{preg}}^T \mathbf{h}), \quad \hat{y}_{\text{avoid}} = \sigma(\mathbf{W}_{\text{avoid}}^T \mathbf{h})

where $\sigma$ denotes the sigmoid function producing probability estimates.

Multi-task learning jointly optimizes pregnancy prediction and embryo quality avoidance classification, leveraging correlation between these outcomes to improve feature learning.

3.6.2 Data Preprocessing and Augmentation

Cropping operations center on segmentation-derived embryo locations, extracting fixed-size regions that normalize spatial positioning. For devices with different optical magnifications, scaling factors derived from known microns-per-pixel ratios standardize spatial scales before feature extraction.

Training augmentation includes random rotations, shifts, scaling, and brightness adjustments to promote invariance to imaging variations while preserving biologically meaningful morphological patterns. Care is taken to avoid augmentations that could alter morphological classifications, such as excessive smoothing that might obscure cellular boundaries.

3.7 Integrated Pipeline Execution

The complete assessment pipeline orchestrates execution of specialized models in a directed acyclic graph structure where downstream tasks depend on outputs from upstream components.

For a given embryo video, processing proceeds as follows:

Stage 1 - Segmentation: Each frame undergoes segmentation to localize the embryo and compute centroid coordinates. Frames lacking valid detections are flagged.

Stage 2 - Validity Filtering: Segmented frames are classified as valid or invalid based on focus quality and artifact presence. Invalid frames are excluded from subsequent analysis.

Stage 3 - Blastocyst Detection: Valid frames are evaluated for blastocyst presence. Detected blastocyst frames proceed to morphological grading.

Stage 4 - Morphological Grading: Blastocyst frames receive quality scores across multiple dimensions. Scores are aggregated across frames to produce temporal profiles of quality evolution.

Stage 5 - Temporal Assessment: Video clips sampled according to task-specific strategies are processed through Video Swin Transformer models to predict developmental outcomes and euploidy likelihood.

Stage 6 - Morphokinetic Analysis: Siamese network embeddings are computed for all frames to detect key developmental events and construct morphokinetic timelines.

Stage 7 - Outcome Prediction: Selected high-quality frames identified in earlier stages serve as input to clinical outcome models predicting pregnancy and viability.

Results from all stages are aggregated into comprehensive embryo reports containing segmentation masks, quality scores, developmental predictions, morphokinetic timelines, and outcome probabilities with associated confidence intervals.

4. Experiments and Results

4.1 Datasets and Training Protocols

The system was trained on multi-center datasets comprising embryo videos from commercial time-lapse imaging platforms. Training data encompassed multiple clinical sites with diverse patient populations and culture protocols to promote generalization.

Segmentation training utilized manually annotated frames with pixel-wise embryo boundary delineations from multiple clinical sites. Blastocyst detection and grading datasets contained labeled frames categorized by developmental stage and quality grades assigned by trained embryologists following Gardner criteria [17]. Day 3 developmental prediction training involved video sequences from embryos with known Day 5 outcomes. Euploidy prediction training used sequences from embryos undergoing preimplantation genetic testing with known chromosomal status.

Data splitting followed patient-level stratification to prevent information leakage between training and validation sets. Test sets reserved 15 percent of patients not used during model development. For temporal models, temporal cross-validation ensured test embryos came from dates chronologically after training data collection periods.

Training employed AdamW optimizer with weight decay regularization and task-specific learning rates determined through preliminary sweeps. Learning rate scheduling reduced rates upon validation loss plateaus. Batch sizes were adjusted to maximize GPU memory utilization while maintaining stable gradient estimates. Early stopping based on validation performance prevented overfitting, with patience values allowing sufficient training epochs to reach convergence.

4.2 Segmentation Performance

Embryo segmentation networks achieved strong Dice coefficients on held-out test sets, indicating substantial overlap between predicted and ground truth masks. Precision and recall metrics demonstrated balanced performance avoiding both over-segmentation and under-segmentation errors.

Qualitative inspection revealed accurate boundary delineation even in challenging cases with poor contrast or adjacent debris particles. The weighted square error loss function proved effective for encouraging precise contour localization rather than approximate bounding regions.

Centroid estimation accuracy provided sufficient precision for downstream cropping operations, enabling reliable embryo-centered region extraction across diverse imaging conditions.

4.3 Blastocyst Detection and Grading Accuracy

Multi-task classification networks for blastocyst detection achieved competitive area under receiver operating characteristic curve (AUROC) values, with ResNet-50 backbones demonstrating performance advantages suitable for clinical deployment. Sensitivity and specificity metrics exhibited balanced operating points appropriate for embryo screening applications.

Morphological grading performance varied by quality dimension. Inner cell mass grading exhibited confusion primarily between adjacent grades rather than distant categories, confirming that the ordinal regression approach successfully encoded label structure. Trophectoderm grading achieved similar performance characteristics. Multi-task learning improved average performance compared to independently trained single-task models, supporting the hypothesis that shared representations benefit related prediction tasks.

Ordinal regression formulations outperformed standard multi-class classification in metrics penalizing rank violations, such as weighted kappa statistics. The quantile-based binning approach using training set percentiles (2.5th and 97.5th) effectively handled class imbalance without requiring explicit class weighting, providing robustness to outliers compared to absolute minimum and maximum normalization.

4.4 Temporal Model Performance

Video Swin Transformer models for Day 3 developmental prediction demonstrated the value of temporal context, with separate prediction heads for high-quality blastocyst formation likelihood (is_fhb target) and poor-quality development risk (is_avoid target). These models outperformed frame-based baseline approaches operating on single timepoints, demonstrating benefits of spatiotemporal reasoning over static morphological analysis.

Ablation studies examining temporal sampling strategies revealed that non-uniform sampling emphasizing early cleavage stages improved performance relative to uniform sampling, consistent with biological knowledge regarding prognostic value of early developmental kinetics. The system employs sampling between 20-72 hours post-insemination for Day 3 prediction tasks.

Window size selection for spatiotemporal attention influenced both accuracy and computational cost. Windows of size 2-by-7-by-7 along temporal, height, and width dimensions respectively provided optimal trade-offs, balancing receptive field coverage with computational efficiency.

Euploidy prediction models operating on pre-biopsy video sequences (60-128 hours post-insemination working range) demonstrated non-invasive prediction capability that, while not replacing genetic testing, could inform embryo prioritization when testing is cost-prohibitive. A two-stage ensemble approach combining Video Swin Transformer predictions with clinical metadata (maternal age, Day 5 morphological scores) using calibrated logistic regression provided enhanced prediction accuracy.

4.5 Morphokinetic Event Detection

Siamese network approaches to morphokinetic event detection successfully identified key developmental milestones including pronuclear fading, two-cell division (t2), four-cell division (t4), and eight-cell division (t8). The hybrid architecture combines a CNN classification head providing initial event predictions with a Siamese distance network that refines timing through frame similarity analysis.

The post-processing algorithm constructs unified event timelines by:

Computing siamese distance signals comparing each frame to temporally adjacent frames

Detecting peaks in the distance profile using adaptive threshold based on running mean and variance

Mapping classifier predictions to establish initial event sequence (t2-t9, tB, tM)

Refining event timing using siamese peaks with isotonic regression to enforce biological plausibility

Performance degraded for later events like compaction (tM) and cavitation where morphological changes occur more gradually, resulting in ambiguous ground truth definitions and wider temporal windows.

Contrastive learning proved effective when labeled event data was limited, as the pairwise training paradigm leveraged unlabeled frames to learn discriminative embeddings capturing morphological transitions.

4.6 Clinical Outcome Prediction

Pregnancy prediction models processing single or multiple embryo frames demonstrated modest predictive capability. Multi-frame aggregation provided improvements over single-frame analysis by capturing morphological dynamics, suggesting temporal evolution patterns contribute predictive signal beyond static appearance features.

Performance on clinical prediction tasks remained more modest than morphological assessment tasks, reflecting the multifactorial nature of outcomes where embryo quality constitutes only one determinant among maternal factors, endometrial receptivity, and transfer procedures that fall outside the model's observational scope. The system implements tabular classifiers combining morphokinetic timing features with segmentation statistics to predict pregnancy probability from developmental kinetics.

4.7 Cross-Device Generalization

Models trained on mixed datasets from multiple imaging platforms (multiple commercial time-lapse imaging platforms) demonstrated robust cross-device performance. The system maintains device-specific preprocessing branches where certain imaging devices employ centroid-based cropping with scale correction, while other imaging platforms use direct spatial resizing, reflecting differences in optical magnification and field of view characteristics.

Preprocessing strategies including spatial normalization and intensity standardization proved critical for cross-device generalization. Fine-tuning pretrained models on device-specific datasets effectively adapted models to new platforms, suggesting practical deployment pathways leveraging large multi-site pretraining followed by site-specific calibration.

5. Discussion

5.1 Strengths and Innovations

The presented system advances the state-of-the-art in automated embryo assessment through several key innovations. The hierarchical pipeline decomposition enables modular optimization of specialized components while maintaining end-to-end consistency. This architecture facilitates independent improvement of individual stages without requiring complete system retraining, accelerating development cycles and enabling targeted debugging.

The integration of spatial and temporal modeling modalities addresses complementary aspects of embryo assessment. Static morphological analysis captures instantaneous quality indicators visible in individual frames, while temporal analysis reveals developmental kinetics patterns unobservable from static snapshots. The combination provides comprehensive assessment beyond either modality in isolation.

The multi-task learning framework efficiently leverages limited labeled data by encouraging shared representation learning across correlated prediction tasks. This approach proves particularly valuable in medical domains where labeling requires expensive expert annotation effort. Joint training of related tasks improves data efficiency while potentially mitigating overfitting to spurious correlations in any single task.

The production deployment architecture demonstrates feasibility of clinical integration through standardized API interfaces, model serving infrastructure supporting concurrent requests, and optimization techniques including model optimization enabling hardware-accelerated inference. Attention to deployment considerations distinguishes research prototypes from clinically viable systems.

5.2 Limitations and Failure Modes

Several limitations warrant acknowledgment. Temporal models exhibit degraded performance on videos with atypical culture durations or anomalous developmental kinetics falling outside training distribution patterns. The fixed sampling strategies may miss critical events occurring at unexpected timepoints, suggesting potential benefits from adaptive sampling schemes guided by preliminary content analysis.

Segmentation accuracy degrades in the presence of severe imaging artifacts, extreme embryo positioning at frame boundaries, or multiple embryos in the same field of view. While these cases constitute a small minority of samples, they require fallback mechanisms or manual review to prevent cascading errors through the pipeline.

Morphological grading performance remains substantially below inter-observer agreement levels exhibited by expert embryologists on reference datasets, suggesting room for improvement through architectural innovations, training procedure refinements, or incorporation of additional data modalities such as polarization imaging when available.

Clinical outcome prediction accuracy, while significantly exceeding random baselines, does not reach thresholds sufficient for autonomous decision-making without clinician oversight. The multifactorial nature of clinical outcomes limits achievable performance when models observe only embryo morphology without access to maternal characteristics, endometrial receptivity markers, or transfer procedure details.

5.3 Potential Improvements and Future Directions

Several avenues for improvement merit investigation. Incorporating uncertainty estimation through techniques such as Monte Carlo dropout, deep ensembles, or evidential deep learning could quantify prediction confidence and flag ambiguous cases for manual review, potentially improving clinical trust and utility.

Multi-modal fusion integrating diverse data sources including clinical metadata, endometrial imaging, and hormonal profiles could address the inherent limitations of embryo-only assessment for outcome prediction. Attention mechanisms could learn to weight information sources based on relevance to specific predictions.

Self-supervised pretraining on large unlabeled video corpora could improve feature learning, particularly for temporal models where labeled outcome data is limited. Contrastive learning objectives based on temporal coherence, video reconstruction, or cross-modal alignment between frames from different focal planes or imaging modalities offer promising pretraining strategies.

Active learning frameworks could optimize annotation effort by intelligently selecting informative samples for labeling, potentially improving model performance with reduced expert time investment compared to random sampling.

Explainability enhancements through attention visualization, gradient-based saliency mapping, or concept-based interpretability could elucidate which morphological features drive predictions, facilitating clinical validation and hypothesis generation regarding biological mechanisms underlying observed correlations.

6. Conclusion

This work presented a comprehensive deep learning-based platform for automated embryo assessment integrating multiple specialized neural architectures spanning segmentation, classification, and temporal reasoning tasks. The hierarchical pipeline processes time-lapse microscopy videos to generate detailed embryo reports including morphological quality scores, developmental predictions, morphokinetic timelines, and clinical outcome probabilities.

Experimental evaluation on multi-center datasets demonstrated competitive performance across a range of assessment tasks, achieving strong segmentation overlap metrics, robust blastocyst detection capability, and effective temporal developmental prediction. The system exhibits robust cross-device generalization through careful preprocessing and mixed-platform training strategies.

Production deployment via model serving infrastructure with standardized APIs enables integration into clinical workflows with real-time inference latency. The modular architecture facilitates ongoing improvement through independent optimization of pipeline components.

While limitations remain, particularly in clinical outcome prediction accuracy and handling of rare edge cases, the presented system advances the state-of-the-art in embryo assessment automation and provides a foundation for future developments incorporating additional data modalities, uncertainty quantification, and enhanced explainability mechanisms.

7. References

[1] Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015.

[2] Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition.

[3] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition.

[4] Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016). 3D U-Net: learning dense volumetric segmentation from sparse annotation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016.

[5] Milletari, F., Navab, N., & Ahmadi, S. A. (2016). V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV).

[6] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Identity mappings in deep residual networks. In European conference on computer vision.

[7] Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition.

[8] Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ... & Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.

[9] Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning?. IEEE transactions on medical imaging, 35(5), 1299-1312.

[10] Raghu, M., Zhang, C., Kleinberg, J., & Bengio, S. (2019). Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems, 32.

[11] Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. nature, 542(7639), 115-118.

[12] Ruder, S. (2017). An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098.

[13] Caruana, R. (1997). Multitask learning. Machine learning, 28, 41-75.

[14] Frank, E., & Hall, M. (2001). A simple approach to ordinal classification. In European conference on machine learning.

[15] Cheng, J., Wang, Z., & Pollastri, G. (2008). A neural network approach to ordinal regression. In 2008 IEEE International Joint Conference on Neural Networks.

[16] Niu, Z., Zhou, M., Wang, L., Gao, X., & Hua, G. (2016). Ordinal regression with multiple output cnn for age estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition.

[17] Gardner, D. K., Lane, M., Stevens, J., Schlenker, T., & Schoolcraft, W. B. (2000). Blastocyst score affects implantation and pregnancy outcome: towards a single blastocyst transfer. Fertility and sterility, 73(6), 1155-1158.

[18] Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015). Learning spatiotemporal features with 3d convolutional networks. In Proceedings of the IEEE international conference on computer vision.

[19] Simonyan, K., & Zisserman, A. (2014). Two-stream convolutional networks for action recognition in videos. Advances in neural information processing systems, 27.

[20] Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., & Van Gool, L. (2016). Temporal segment networks: Towards good practices for deep action recognition. In European conference on computer vision.

[21] Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lučić, M., & Schmid, C. (2021). Vivit: A video vision transformer. In Proceedings of the IEEE/CVF international conference on computer vision.

[22] Liu, Z., Ning, J., Cao, Y., Wei, Y., Zhang, Z., Lin, S., & Hu, H. (2022). Video swin transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

[23] Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., & Shah, R. (1993). Signature verification using a "siamese" time delay neural network. Advances in neural information processing systems, 6.

[24] Chopra, S., Hadsell, R., & LeCun, Y. (2005). Learning a similarity metric discriminatively, with application to face verification. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[25] Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition.

[26] Koch, G., Zemel, R., & Salakhutdinov, R. (2015). Siamese neural networks for one-shot image recognition. In ICML deep learning workshop.

[27] Santos Filho, E., Noble, J. A., & Wells, D. (2010). A review on automatic analysis of human embryo microscope images. The Open Biomedical Engineering Journal, 4, 170.

[28] Meseguer, M., Herrero, J., Tejera, A., Hilligsøe, K. M., Ramsing, N. B., & Remohí, J. (2011). The use of morphokinetics as a predictor of embryo implantation. Human reproduction, 26(10), 2658-2671.

[29] Kragh, M. F., Rimestad, J., Lassen, J. T., & Karstoft, H. (2019). Automatic grading of human blastocysts from time-lapse imaging. Computers in biology and medicine, 115, 103494.

[30] Tran, D., Cooke, S., Illingworth, P. J., & Gardner, D. K. (2019). Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer. Human reproduction, 34(6), 1011-1018.