Morphometric evaluation of two-pronucleus zygote images using image-processing techniques.

Identifying embryos with a high potential for implementation remains a challenge in in vitro fertilization (IVF) cycles. Despite progress in IVF treatment, only a minority of generated embryos has the ability to implant. Another drawback of this practice is the high frequency of multiple pregnancies. This problem leads to economic and health problems. Therefore, the transfer of a single embryo with high implantation potential is the ideal strategy. Morphometric evaluation of two-pronucleus zygote images is a helpful technique when aiming to transfer a single embryo with a high implantation potential. In this study, an automated zygote morphometric evaluation algorithm, called the zygote morphology evaluation (ZME) algorithm, was created to analyze the zygote and provide morphological measurements. The first and most crucial step of the ZME algorithm is the noise reduction step, which was first applied to zygote images. After that, the proposed algorithm detects different parts of the zygote that are indicators of embryo viability and normality, that is the oolemma, perivitelline space, zona pellucida, and nucleolar precursor bodies (NPBs). In addition, a novel dataset was prepared for this task. This dataset consisted of 703 human zygote images, and called the human zygote morphometric evaluation dataset (HZME-DS). Our experimental results in the HZME-DS showed that the ZME algorithm was able to achieve 79.58% average accuracy in identifying the oolemma region, 79.40% average accuracy in determining the perivitelline space, and 79.72% accuracy in identifying the zona pellucida. To calculate the accuracy of identifying NPBs, the proposed algorithm uses Recall and Precision measures, and their harmonic average (F1 measure) reached values of 81.14% and 79.53%, respectively. These encouraging results for our proposed method, which is an automatic and very fast method, showed that the ZME algorithm could help embryologists to evaluate the best zygotes in real time and the best embryos subsequently.

A robust deep learning-based multiclass segmentation method for analyzing human metaphase II oocyte images.

BACKGROUND AND OBJECTIVE: The morphology of the human metaphase II (MII) oocyte is an essential indicator of the embryo's potential for developing into a healthy baby in the Intra-Cytoplasmic Sperm Injection (ICSI) process. In this case, characteristics such as oocyte and ooplasm area, zona pellucida (ZP) thickness, and perivitelline space (PVS) width are also linked to the embryo's implantation potential. Moreover, oocyte segmentation methods may be of particular interest in those countries' restrictive IVF legislation. METHODS: While the manual examination is impractically time-consuming and subjective, this paper concentrates efforts on designing an automated deep learning framework to take on the challenging task of segmentation in low-resolution microscopic images of MII oocytes. In particular, we have developed a deep learning network based on an improved U-Net model using our presented unique collection of human MII oocyte images (a new challenging dataset contains 1,009 images accompanied by manually labeled pixel-accurate ground truths). High-quality ground truth (GT) preparation is a labor-intensive task. However, we put considerable effort into assessing how different types of GT annotations (binary and multiclass) impact segmentation performance. RESULTS: Experimental results on 250 MII oocyte test images demonstrate that the proposed multiclass segmentation algorithm is able to segment complex and irregular ooplasm, ZP, and PVS structures more accurately than its two-class version. Furthermore, the proposed architecture outperforms two other state-of-the-art deep learning models, U-Net and ENet, for the MII oocyte segmentation task. CONCLUSIONS: The findings of this study provide a fascinating insight into the automatic and accurate segmentation of human MII oocytes.

Effect of deep transfer and multi-task learning on sperm abnormality detection.

Analyzing the abnormality of morphological characteristics of male human sperm has been studied for a long time mainly because it has many implications on the male infertility problem, which accounts for approximately half of the infertility problems in the world. Yet, detecting such abnormalities by embryologists has several downsides. To clarify, analyzing sperms through visual inspection of an expert embryologist is a highly subjective and biased process. Furthermore, it takes much time for a specialist to make a diagnosis. Hence, in this paper, we proposed two deep learning algorithms that are able to automate this process. The first algorithm uses a network-based deep transfer learning approach, while the second technique, named Deep Multi-task Transfer Learning (DMTL), employs a novel combination of network-based deep transfer learning and multi-task learning to classify sperm's head, vacuole, and acrosome as either normal or abnormal. This DMTL technique is capable of classifying all the aforementioned parts of the sperm in a single prediction. Moreover, this is the first time that the concept of multi-task learning has been introduced to the field of Sperm Morphology Analysis (SMA). To benchmark our algorithms, we employed a freely-available SMA dataset named MHSMA. During our experiments, our algorithms reached the state-of-the-art results on the accuracy, precision, and f0.5, as well as other important metrics, such as the Matthews Correlation Coefficient on one, two, or all three labels. Notably, our algorithms increased the accuracy of the head, acrosome, and vacuole by 6.66%, 3.00%, and 1.33%, and reached the accuracy of 84.00%, 80.66%, and 94.00% on these labels, respectively. Consequently, our algorithms can be used in health institutions, such as fertility clinics, with further recommendations to practically improve the performance of our algorithms.

A novel deep learning method for automatic assessment of human sperm images.

Sperm morphology analysis (SMA) is a very important factor in the diagnosis process of male infertility. This research proposes a novel deep learning algorithm for malformation detection of sperm morphology using human sperm cell images. Our proposed method detects and analyzes different parts of human sperms. First of all, we have prepared an image collection, called the MHSMA dataset, which can be used as a standard benchmark for future machine learning studies in this problem. This collection consists of 1,540 sperm images from 235 patients with male factor infertility. This unique dataset is freely available to the public. After applying data augmentation techniques, we have proposed a sampling method for fixing data imbalance. Then, we have designed a deep neural network architecture and trained it to detect morphological deformities in different parts of human sperm-head, acrosome, and vacuole. Our proposed method is one of the first algorithms that considers the acrosome. In addition, our method can work very well with non-stained and low-resolution images. Our experimental results on the proposed benchmark show the high accuracy of our deep learning algorithm for detection of morphological deformities from images. In these experiments, the proposed algorithm has achieved F0.5 scores of 84.74%, 83.86%, and 94.65% in acrosome, head, and vacuole abnormality detection, respectively. It should be noted that our algorithm achieves a better accuracy than existing state-of-the-art methods in acrosome and vacuole abnormality detection on the proposed benchmark. Also, our method works very fast. It can classify images in real-time, even on a mainstream laptop computer. This allows an embryologist to quickly decide whether or not the analyzed sperm should be selected.

A novel method for predicting kidney stone type using ensemble learning.

The high morbidity rate associated with kidney stone disease, which is a silent killer, is one of the main concerns in healthcare systems all over the world. Advanced data mining techniques such as classification can help in the early prediction of this disease and reduce its incidence and associated costs. The objective of the present study is to derive a model for the early detection of the type of kidney stone and the most influential parameters with the aim of providing a decision-support system. Information was collected from 936 patients with nephrolithiasis at the kidney center of the Razi Hospital in Rasht from 2012 through 2016. The prepared dataset included 42 features. Data pre-processing was the first step toward extracting the relevant features. The collected data was analyzed with Weka software, and various data mining models were used to prepare a predictive model. Various data mining algorithms such as the Bayesian model, different types of Decision Trees, Artificial Neural Networks, and Rule-based classifiers were used in these models. We also proposed four models based on ensemble learning to improve the accuracy of each learning algorithm. In addition, a novel technique for combining individual classifiers in ensemble learning was proposed. In this technique, for each individual classifier, a weight is assigned based on our proposed genetic algorithm based method. The generated knowledge was evaluated using a 10-fold cross-validation technique based on standard measures. However, the assessment of each feature for building a predictive model was another significant challenge. The predictive strength of each feature for creating a reproducible outcome was also investigated. Regarding the applied models, parameters such as sex, acid uric condition, calcium level, hypertension, diabetes, nausea and vomiting, flank pain, and urinary tract infection (UTI) were the most vital parameters for predicting the chance of nephrolithiasis. The final ensemble-based model (with an accuracy of 97.1%) was a robust one and could be safely applied to future studies to predict the chances of developing nephrolithiasis. This model provides a novel way to study stone disease by deciphering the complex interaction among different biological variables, thus helping in an early identification and reduction in diagnosis time.

Applying data mining techniques for increasing implantation rate by selecting best sperms for intra-cytoplasmic sperm injection treatment.

BACKGROUND AND OBJECTIVE: Aspiration of a good-quality sperm during intracytoplasmic sperm injection (ICSI) is one of the main concerns. Understanding the influence of individual sperm morphology on fertilization, embryo quality, and pregnancy probability is one of the most important subjects in male factor infertility. Embryologists need to decide the best sperm for injection in real time during ICSI cycle. Our objective is to predict the quality of zygote, embryo, and implantation outcome before injection of each sperm in an ICSI cycle for male factor infertility with the aim of providing a decision support system on the sperm selection. METHODS: The information was collected from 219 patients with male factor infertility at the infertility therapy center of Alzahra hospital in Rasht from 2012 through 2014. The prepared dataset included the quality of zygote, embryo, and implantation outcome of 1544 injected sperms into the related oocytes. In our study, embryo transfer was performed at day 3. Each sperm was represented with thirteen clinical features. Data preprocessing was the first step in the proposed data mining algorithm. After applying more than 30 classifiers, 9 successful classifiers were selected and evaluated by 10-fold cross validation technique using precision, recall, F1, and AUC measures. Another important experiment was measuring the effect of each feature in prediction process. RESULTS: In zygote and embryo quality prediction, IBK and RandomCommittee models provided 79.2% and 83.8% F1, respectively. In implantation outcome prediction, KStar model achieved 95.9% F1, which is even better than prediction of human experts. All these predictions can be done in real time. CONCLUSIONS: A machine learning-based decision support system would be helpful in sperm selection phase of ICSI cycle to improve the success rate of ICSI treatment.

An efficient method for automatic morphological abnormality detection from human sperm images.

BACKGROUND AND OBJECTIVE: Sperm morphology analysis (SMA) is an important factor in the diagnosis of human male infertility. This study presents an automatic algorithm for sperm morphology analysis (to detect malformation) using images of human sperm cells. METHODS: The SMA method was used to detect and analyze different parts of the human sperm. First of all, SMA removes the image noises and enhances the contrast of the image to a great extent. Then it recognizes the different parts of sperm (e.g., head, tail) and analyzes the size and shape of each part. Finally, the algorithm classifies each sperm as normal or abnormal. Malformations in the head, midpiece, and tail of a sperm, can be detected by the SMA method. In contrast to other similar methods, the SMA method can work with low resolution and non-stained images. Furthermore, an image collection created for the SMA, has also been described in this study. This benchmark consists of 1457 sperm images from 235 patients, and is known as human sperm morphology analysis dataset (HSMA-DS). RESULTS: The proposed algorithm was tested on HSMA-DS. The experimental results show the high ability of SMA to detect morphological deformities from sperm images. In this study, the SMA algorithm produced above 90% accuracy in sperm abnormality detection task. Another advantage of the proposed method is its low computation time (that is, less than 9s), as such, the expert can quickly decide to choose the analyzed sperm or select another one. CONCLUSIONS: Automatic and fast analysis of human sperm morphology can be useful during intracytoplasmic sperm injection for helping embryologists to select the best sperm in real time.