Human Blastocyst\u27s Zona Pellucida Segmentation via Boosting Ensemble of Complementary Learning

Characteristics of Zona Pellucida (ZP), particularly its thickness, is a key indicator of human blastocyst (day-5embryo) quality. Therefore, ZP segmentation is an important step towards achieving automatic embryo qualityassessment. In this paper, a novel approach based on boosting ensemble of hybrid complementary learning isproposed to segment Zona Pellucida in human blastocyst images. First, an inner-ZP localization method isproposed to separate the ZP from the heavily textured area inside a blastocyst. Then, a deep Hierarchical NeuralNetwork (HiNN) is proposed to segment ZP area. The hierarchical nature of the proposed network enableslearning features with respect to their spatial location in the embryo. Finally, a Self-supervised Image-SpecificRefinement (SISR) strategy is proposed as a complementary step to boost the performance. The proposed systemis a hybrid approach in the sense that the HiNN learns the intra-correlation among images, while the SISR takesinto account the inter-correlation within the query image. Experimental results confirm that the proposed method is capable of identifying ZP area with average Precision, Recall, Accuracy and Jaccard Index of 85.2%, 92.0%, 95.6% and 78.1%, respectively. The proposed HiNN system outperforms state of the art by 4.9% in Precision, 11.2% in Recall, 3.6% in Accuracy and 10.7% in Jaccard Index

Cell-Net: Embryonic Cell Counting and Centroid Localization via Residual Incremental Atrous Pyramid and Progressive Upsampling Convolution

In-vitro fertilization (IVF), as the most common fertility treatment, has never reached its maximum potentials. Systematic selection of embryos with the highest implementation potentials is a necessary step towards enhancing the effectiveness of IVF. Embryonic cell numbers and their developmental rate are believed to correlate with the embryo\u27s implantation potentials. In this paper, we propose an automatic framework based on a deep convolutional neural network to take on the challenging task of automatic counting and centroid localization of embryonic cells (blastomeres) in microscopic human embryo images. In particular, the cell counting task is reformulated as an end-to-end regression problem that is based on a shape-aware Gaussian dot annotation to map the input image into an output density map. The proposed Cell-Net system incorporates two novel components, residual incremental Atrous pyramid and progressive up-sampling convolution. Residual incremental Atrous pyramid enables the network to extract rich global contextual information without raising the ‘grinding’ issue. Progressive up-sampling convolution gradually reconstructs a high-resolution feature map by taking into account short- and longrange dependencies. Experimental results confirm that the proposed framework is capable of predicting the cell-stage and detecting blastomeres in embryo images of

Individualized versus conventional ovarian stimulation for in vitro fertilization: a multicenter, randomized, controlled, assessor-blinded, phase 3 noninferiority trial

Objective To compare the efficacy and safety of follitropin delta, a new human recombinant FSH with individualized dosing based on serum antimüllerian hormone (AMH) and body weight, with conventional follitropin alfa dosing for ovarian stimulation in women undergoing IVF. Design Randomized, multicenter, assessor-blinded, noninferiority trial (ESTHER-1). Setting Reproductive medicine clinics. Patient(s) A total of 1,329 women (aged 18â40 years). Intervention(s) Follitropin delta (AMH <15 pmol/L: 12 μg/d; AMH â¥15 pmol/L: 0.10â0.19 μg/kg/d; maximum 12 μg/d), or follitropin alfa (150 IU/d for 5 days, potential subsequent dose adjustments; maximum 450 IU/d). Main Outcomes Measure(s) Ongoing pregnancy and ongoing implantation rates; noninferiority margins â8.0%. Result(s) Ongoing pregnancy (30.7% vs. 31.6%; difference â0.9% [95% confidence interval (CI) â5.9% to 4.1%]), ongoing implantation (35.2% vs. 35.8%; â0.6% [95% CI â6.1% to 4.8%]), and live birth (29.8% vs. 30.7%; â0.9% [95% CI â5.8% to 4.0%]) rates were similar for individualized follitropin delta and conventional follitropin alfa. Individualized follitropin delta resulted in more women with target response (8â14 oocytes) (43.3% vs. 38.4%), fewer poor responses (fewer than four oocytes in patients with AMH <15 pmol/L) (11.8% vs. 17.9%), fewer excessive responses (â¥15 or â¥20 oocytes in patients with AMH â¥15 pmol/L) (27.9% vs. 35.1% and 10.1% vs. 15.6%, respectively), and fewer measures taken to prevent ovarian hyperstimulation syndrome (2.3% vs. 4.5%), despite similar oocyte yield (10.0 ± 5.6 vs. 10.4 ± 6.5) and similar blastocyst numbers (3.3 ± 2.8 vs. 3.5 ± 3.2), and less gonadotropin use (90.0 ± 25.3 vs. 103.7 ± 33.6 μg). Conclusion(s) Optimizing ovarian response in IVF by individualized dosing according to pretreatment patient characteristics results in similar efficacy and improved safety compared with conventional ovarian stimulation. Clinical Trial Registration Number NCT01956110

Human Blastocyst's Zona Pellucida segmentation via boosting ensemble of complementary learning

Characteristics of Zona Pellucida (ZP), particularly its thickness, is a key indicator of human blastocyst (day-5 embryo) quality. Therefore, ZP segmentation is an important step towards achieving automatic embryo quality assessment. In this paper, a novel approach based on boosting ensemble of hybrid complementary learning is proposed to segment Zona Pellucida in human blastocyst images. First, an inner-ZP localization method is proposed to separate the ZP from the heavily textured area inside a blastocyst. Then, a deep Hierarchical Neural Network (HiNN) is proposed to segment ZP area. The hierarchical nature of the proposed network enables learning features with respect to their spatial location in the embryo. Finally, a Self-supervised Image-Specific Refinement (SISR) strategy is proposed as a complementary step to boost the performance. The proposed system is a hybrid approach in the sense that the HiNN learns the intra-correlation among images, while the SISR takes into account the inter-correlation within the query image. Experimental results confirm that the proposed method is capable of identifying ZP area with average Precision, Recall, Accuracy and Jaccard Index of 85.2%, 92.0%, 95.6% and 78.1%, respectively. The proposed HiNN system outperforms state of the art by 4.9% in Precision, 11.2% in Recall, 3.6% in Accuracy and 10.7% in Jaccard Index. Keywords: Zona pellucida, Human embryo, IVF, Medical image analysis, Deep neural networ

A novel lung explant model for the ex vivo study of efficacy and mechanisms of anti-influenza drugs

Influenza A virus causes considerable morbidity and mortality largely because of a lack of effective antiviral drugs. Viral neuraminidase inhibitors, which inhibit viral release from the infected cell, are currently the only approved drugs for influenza, but have recently been shown to be less effective than previously thought. Growing resistance to therapies that target viral proteins has led to increased urgency in the search for novel anti-influenza compounds. However, discovery and development of new drugs have been restricted because of differences in susceptibility to influenza between animal models and humans and a lack of translation between cell culture and in vivo measures of efficacy. To circumvent these limitations, we developed an experimental approach based on ex vivo infection of human bronchial tissue explants and optimized a method of flow cytometric analysis to directly quantify infection rates in bronchial epithelial tissues. This allowed testing of the effectiveness of TVB024, a vATPase inhibitor that inhibits viral replication rather than virus release, and to compare efficacy with the current frontline neuraminidase inhibitor, oseltamivir. The study showed that the vATPase inhibitor completely abrogated epithelial cell infection, virus shedding, and the associated induction of proinflammatory mediators, whereas oseltamivir was only partially effective at reducing these mediators and ineffective against innate responses. We propose, therefore, that this explant model could be used to predict the efficacy of novel anti-influenza compounds targeting diverse stages of the viral replication cycle, thereby complementing animal models and facilitating progression of new drugs into clinical trials