A unified graphical models framework for automated mitosis detection in human embryos

Automatic characterization of human embryos at day 4 post-insemination from time-lapse imaging using supervised contrastive learning and inductive transfer learning techniques

BACKGROUND

Embryo morphology is a predictive marker for implantation success and ultimately live births. Viability evaluation and quality grading are commonly used to select the embryo with the highest implantation potential. However, the traditional method of manual embryo assessment is time-consuming and highly susceptible to inter- and intra-observer variability. Automation of this process results in more objective and accurate predictions.

METHOD

In this paper, we propose a novel methodology based on deep learning to automatically evaluate the morphological appearance of human embryos from time-lapse imaging. A supervised contrastive learning framework is implemented to predict embryo viability at day 4 and day 5, and an inductive transfer approach is applied to classify embryo quality at both times.

RESULTS

Results showed that both methods outperformed conventional approaches and improved state-of-the-art embryology results for an independent test set. The viability result achieved an accuracy of 0.8103 and 0.9330 and the quality results reached values of 0.7500 and 0.8001 for day 4 and day 5, respectively. Furthermore, qualitative results kept consistency with the clinical interpretation.

CONCLUSIONS

The proposed methods are up to date with the artificial intelligence literature and have been proven to be promising. Furthermore, our findings represent a breakthrough in the field of embryology in that they study the possibilities of embryo selection at day 4. Moreover, the grad-CAMs findings are directly in line with embryologists' decisions. Finally, our results demonstrated excellent potential for the inclusion of the models in clinical practice.

Embryo Grading With Unreliable Labels Due to Chromosome Abnormalities by Regularized PU Learning With Ranking

We propose a method for human embryo grading with its images. This grading has been achieved by positive-negative classification (i.e., live birth or non-live birth). However, negative (non-live birth) labels collected in clinical practice are unreliable because the visual features of negative images are equal to those of positive (live birth) images if these non-live birth embryos have chromosome abnormalities. For alleviating an adverse effect of these unreliable labels, our method employs Positive-Unlabeled (PU) learning so that live birth and non-live birth are labeled as positive and unlabeled, respectively, where unlabeled samples contain both positive and negative samples. In our method, this PU learning on a deep CNN is improved by a learning-to-rank scheme. While the original learning-to-rank scheme is designed for positive-negative learning, it is extended to PU learning. Furthermore, overfitting in this PU learning is alleviated by regularization with mutual information. Experimental results with 643 time-lapse image sequences demonstrate the effectiveness of our framework in terms of the recognition accuracy and the interpretability. In quantitative comparison, the full version of our proposed method outperforms positive-negative classification in recall and F-measure by a wide margin (0.22 vs. 0.69 in recall and 0.27 vs. 0.42 in F-measure). In qualitative evaluation, visual attentions estimated by our method are interpretable in comparison with morphological assessments in clinical practice.

Automatic grading of human blastocysts from time-lapse imaging

BACKGROUND

Blastocyst morphology is a predictive marker for implantation success of in vitro fertilized human embryos. Morphology grading is therefore commonly used to select the embryo with the highest implantation potential. One of the challenges, however, is that morphology grading can be highly subjective when performed manually by embryologists. Grading systems generally discretize a continuous scale of low to high score, resulting in floating and unclear boundaries between grading categories. Manual annotations therefore suffer from large inter-and intra-observer variances.

METHOD

In this paper, we propose a method based on deep learning to automatically grade the morphological appearance of human blastocysts from time-lapse imaging. A convolutional neural network is trained to jointly predict inner cell mass (ICM) and trophectoderm (TE) grades from a single image frame, and a recurrent neural network is applied on top to incorporate temporal information of the expanding blastocysts from multiple frames.

RESULTS

Results showed that the method achieved above human-level accuracies when evaluated on majority votes from an independent test set labeled by multiple embryologists. Furthermore, when evaluating implantation rates for embryos grouped by morphology grades, human embryologists and our method had a similar correlation between predicted embryo quality and pregnancy outcome.

CONCLUSIONS

The proposed method has shown improved performance of predicting ICM and TE grades on human blastocysts when utilizing temporal information available with time-lapse imaging. The algorithm is considered at least on par with human embryologists on quality estimation, as it performed better than the average human embryologist at ICM and TE prediction and provided a slightly better correlation between predicted embryo quality and implantability than human embryologists.

Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images

Accurate segmentation of cervical cells in Pap smear images is an important step in automatic pre-cancer identification in the uterine cervix. One of the major segmentation challenges is overlapping of cytoplasm, which has not been well-addressed in previous studies. To tackle the overlapping issue, this paper proposes a learning-based method with robust shape priors to segment individual cell in Pap smear images to support automatic monitoring of changes in cells, which is a vital prerequisite of early detection of cervical cancer. We define this splitting problem as a discrete labeling task for multiple cells with a suitable cost function. The labeling results are then fed into our dynamic multi-template deformation model for further boundary refinement. Multi-scale deep convolutional networks are adopted to learn the diverse cell appearance features. We also incorporated high-level shape information to guide segmentation where cell boundary might be weak or lost due to cell overlapping. An evaluation carried out using two different datasets demonstrates the superiority of our proposed method over the state-of-the-art methods in terms of segmentation accuracy.