Image Features Research Articles

Object Detection Algorithms Based on Deep Learning: A Review

With the continuous development of deep learning, object detection algorithms based on deep learning have made significant progress in the field of computer vision, widely applied in areas such as autonomous driving, industrial inspection, agriculture, transportation, and medicine. Traditional object detection algorithms face issues such as low detection efficiency and poor robustness. However, deep learning-based object detection algorithms significantly enhance detection accuracy and generalization by learning low-level and high-level image features. This article first introduces traditional object detection algorithms and their existing problems, then elaborates on the main processes, innovations, advantages, disadvantages, and experimental results on datasets of deep learning-based object detection algorithms. It focuses on the development of Two-Stage and One-Stage object detection algorithms, and provides an outlook on the future development of object detection algorithms, discussing challenges such as the coordination of detection speed and accuracy, difficulties in detecting small objects, real-time detection tasks, and multi-modal fusion applications, and proposes possible future directions.

Artificial intelligence facilitates clinical management of epithelial dysplasia in multiple organs

Epithelial dysplasia is a condition characterized by a spectrum of architectural and cytological alterations to the epithelium, resulting from the accumulation of genetic alterations. It is associated with an increased risk of cancer progression in a variety of organs. However, the variability of different grading systems, as well as inter- and intra-examiner variability, gives rise to concerns regarding the reliability of the results. Histopathology represents the gold standard for the diagnosis of epithelial dysplasia. The combination of big data in pathology and artificial intelligence (AI) will facilitate the achievement of accurate diagnoses and treatments, providing objective and efficient methods to integrate and refine diverse morphological, molecular, and multi-omics information. This perspective provides a summary of the existing research and prospects for the application of AI to epithelial dysplasia in multiple organs. A number of studies have been conducted with the aim of developing a grading system and prognostic identification method for epithelial dysplasia in the oral cavity, larynx, esophagus, and stomach. Digital pathology-based AI may prove useful in facilitating the clinical management of epithelial dysplasia in multiple organs. In summary, digital pathology images obtained by scanning hematoxylin & eosin-stained slides, identifying image features, and building AI models using deep learning combined with machine learning algorithms, validated with real-world data from multicenter cohorts could provide AI as a promising clinical application in the future.

Object Detection Algorithms Based on Deep Learning: A Review

With the continuous development of deep learning, object detection algorithms based on deep learning have made significant progress in the field of computer vision, widely applied in areas such as autonomous driving, industrial inspection, agriculture, transportation, and medicine. Traditional object detection algorithms face issues such as low detection efficiency and poor robustness. However, deep learning-based object detection algorithms significantly enhance detection accuracy and generalization by learning low-level and high-level image features. This article first introduces traditional object detection algorithms and their existing problems, then elaborates on the main processes, innovations, advantages, disadvantages, and experimental results on datasets of deep learning-based object detection algorithms. It focuses on the development of Two-Stage and One-Stage object detection algorithms, and provides an outlook on the future development of object detection algorithms, discussing challenges such as the coordination of detection speed and accuracy, difficulties in detecting small objects, real-time detection tasks, and multi-modal fusion applications, and proposes possible future directions.

Generalizing event-based HDR imaging to various exposures

Single-exposure High Dynamic Range Imaging (HDRI), as a typical ill-posed problem, has attracted extensive attention from researchers. However, restoration in real-world scenarios has always been an intractable task due to various exposures and noise artifacts. This work proposes an event-based HDRI framework that generalizes to scenes under various exposures by exploiting the high dynamic range of events. To address the challenge of processing diverse exposures, we propose an exposure-aware network incorporating the exposure attention fusion module, which facilitates the adaptive fusion of SDR image and event features. Moreover, the problem of noise in extremely under-exposed regions and events is effectively alleviated by introducing a self-supervised loss, namely EDDN, which effectively enhances the details of saturated areas while simultaneously decreasing noise. We conduct novel event-based HDRI datasets to evaluate our proposed method for benchmarking with diverse exposed images. Comprehensive experiments have demonstrated that our method outperforms the state-of-the-art.

Advanced enhancement technique for infrared images of wind turbine blades utilizing adaptive difference multi-scale top-hat transformation

Enhancing infrared images is essential for detecting wind turbine blades using infrared technology. This paper introduces an Infrared Image Enhancement Method based on Adaptive Iterative Cutoff Threshold Difference Multi-Scale Top-Hat Transformation (AICT-DMTH) to address the challenge of low image clarity in infrared detection. The method involves performing a black-white difference top-hat transformation by utilizing structural elements of varying scales for dilation and erosion. Additionally, an iterative threshold method is applied to extract more detailed image features, followed by setting a cutoff constant to determine the final scale of the structural element. The effectiveness of the proposed method is evaluated both qualitatively and quantitatively, with infrared images from laboratory and wind farm settings enhanced and compared against existing methods. The experimental results indicate that the proposed method significantly improves the clarity of infrared images, demonstrating robustness in enhancing images from various environments.

Depth Prior-Guided 3D Voxel Feature Fusion for 3D Semantic Estimation from Monocular Videos

Existing 3D semantic scene reconstruction methods utilize the same set of features extracted from deep learning networks for both 3D semantic estimation and geometry reconstruction, ignoring the differing requirements of semantic segmentation and geometry construction tasks. Additionally, current methods allocate 2D image features to all voxels along camera rays during the back-projection process, without accounting for empty or occluded voxels. To address these issues, we propose separating the features for 3D semantic estimation from those for 3D mesh reconstruction. We use a pretrained vision transformer network for image feature extraction and depth priors estimated by a pretrained multi-view stereo-network to guide the allocation of image features within 3D voxels during the back-projection process. The back-projected image features are aggregated within each 3D voxel via averaging, creating coherent voxel features. The resulting 3D feature volume, composed of unified voxel feature vectors, is fed into a 3D CNN with a semantic classification head to produce a 3D semantic volume. This volume can be combined with existing 3D mesh reconstruction networks to produce a 3D semantic mesh. Experimental results on real-world datasets demonstrate that the proposed method significantly increases 3D semantic estimation accuracy.

EC-YOLO: Improved YOLOv7 Model for PCB Electronic Component Detection.

Electronic components are the main components of PCBs (printed circuit boards), so the detection and classification of ECs (electronic components) is an important aspect of recycling used PCBs. However, due to the variety and quantity of ECs, traditional target detection methods for EC classification still have problems such as slow detection speed and low performance, and the accuracy of the detection needs to be improved. To overcome these limitations, this study proposes an enhanced YOLO (you only look once) network (EC-YOLOv7) for detecting EC targets. The network uses ACmix (a mixed model that enjoys the benefits of both self-attention and convolution) as a substitute for the 3 × 3 convolutional modules in the E-ELAN (Extended ELAN) architecture and implements branch links and 1 × 1 convolutional arrays between the ACmix modules to improve the speed of feature retrieval and network inference. Furthermore, the ResNet-ACmix module is engineered to prevent the leakage of function data and to minimise calculation time. Subsequently, the SPPCSPS (spatial pyramid pooling connected spatial pyramid convolution) block has been improved by replacing the serial channels with concurrent channels, which improves the fusion speed of the image features. To effectively capture spatial information and improve detection accuracy, the DyHead (the dynamic head) is utilised to enhance the model's size, mission, and sense of space, which effectively captures spatial information and improves the detection accuracy. A new bounding-box loss regression method, the WIoU-Soft-NMS method, is finally suggested to facilitate prediction regression and improve the localisation accuracy. The experimental results demonstrate that the enhanced YOLOv7 net surpasses the initial YOLOv7 model and other common EC detection methods. The proposed EC-YOLOv7 network reaches a mean accuracy (mAP@0.5) of 94.4% on the PCB dataset and exhibits higher FPS compared to the original YOLOv7 model. In conclusion, it can significantly enhance high-density EC target recognition.

Fuzzy S-Transform is used for Identifying Image Borders of the Medial Model Mycosic Fungoides

In order to identify mycosis fungoides in medical photos, the researchers used an algorithm. There are several procedures that the detection system needs to take in order to identify cell mycosis fungoides. Mycosis fungoides image features have been studied using the new fuzzy transform because of the function's significance in accurate stage analysis. The statistical properties that were taken into consideration were energy, homogeneity, contrast, correlation, median, mean, entropy, and homogeneity. It has been confirmed that these statistical traits may be used to differentiate across various mycosis fungoides time periods.. We relied on the persistence function since it provides more precise examination of affected regions. Orthogonal conversion was found to be effective in assessing pixel area without changing image properties, allowing for the diagnosis of various illness stages.

Fuzzy S-Transform is used for Identifying Image Borders of the Medial Model Mycosic Fungoides

In order to identify mycosis fungoides in medical photos, the researchers used an algorithm. There are several procedures that the detection system needs to take in order to identify cell mycosis fungoides. Mycosis fungoides image features have been studied using the new fuzzy transform because of the function's significance in accurate stage analysis. The statistical properties that were taken into consideration were energy, homogeneity, contrast, correlation, median, mean, entropy, and homogeneity. It has been confirmed that these statistical traits may be used to differentiate across various mycosis fungoides time periods.. We relied on the persistence function since it provides more precise examination of affected regions. Orthogonal conversion was found to be effective in assessing pixel area without changing image properties, allowing for the diagnosis of various illness stages.

Latent Diffusion Models with Image-Derived Annotations for Enhanced AI-Assisted Cancer Diagnosis in Histopathology.

Artificial Intelligence (AI)-based image analysis has immense potential to support diagnostic histopathology, including cancer diagnostics. However, developing supervised AI methods requires large-scale annotated datasets. A potentially powerful solution is to augment training data with synthetic data. Latent diffusion models, which can generate high-quality, diverse synthetic images, are promising. However, the most common implementations rely on detailed textual descriptions, which are not generally available in this domain. This work proposes a method that constructs structured textual prompts from automatically extracted image features. We experiment with the PCam dataset, composed of tissue patches only loosely annotated as healthy or cancerous. We show that including image-derived features in the prompt, as opposed to only healthy and cancerous labels, improves the Fréchet Inception Distance (FID) by 88.6. We also show that pathologists find it challenging to detect synthetic images, with a median sensitivity/specificity of 0.55/0.55. Finally, we show that synthetic data effectively train AI models.

Copy-move forgery detection algorithm based on binarized statistical image features and principal component analysis

Copy-move forgery detection algorithm based on binarized statistical image features and principal component analysis

Multi-scale object equalization learning network for intracerebral hemorrhage region segmentation

Segmentation and the subsequent quantitative assessment of the target object in computed tomography (CT) images provide valuable information for the analysis of intracerebral hemorrhage (ICH) pathology. However, most existing methods lack a reasonable strategy to explore the discriminative semantics of multi-scale ICH regions, making it difficult to address the challenge of complex morphology in clinical data. In this paper, we propose a novel multi-scale object equalization learning network (MOEL-Net) for accurate ICH region segmentation. Specifically, we first introduce a shallow feature extraction module (SFEM) for obtaining shallow semantic representations to maintain sufficient and effective detailed location information. Then, a deep feature extraction module (DFEM) is leveraged to extract the deep semantic information of the ICH region from the combination of SFEM and original image features. To further achieve equalization learning in different scales of ICH regions, we introduce a multi-level semantic feature equalization fusion module (MSFEFM), which explores the equalized fusion features of the described objects with the assistance of shallow and deep semantic information provided by SFEM and DFEM. Driven by the above three designs, MOEL-Net shows a solid capacity to capture more discriminative features in various ICH region segmentation. To promote the research of clinical automatic ICH region segmentation, we collect two datasets, VMICH and FRICH (divided into Test A and Test B) for evaluation. Experimental results show that the proposed model achieves the Dice scores of 88.28%, 90.92%, and 90.95% on the VMICH, FRICH Test A, and Test B, respectively, which outperform fourteen competing methods.

A privacy-preserving image retrieval scheme with access control based on searchable encryption in media cloud

With the popularity of the media cloud computing industry, individuals and organizations outsource image computation and storage to the media cloud server to reduce the storage burden. Media images usually contain a large amount of private information. To prevent disclosure of privacy of the image owners, media images are encrypted before uploading to the server. However, this operation will greatly limit the utilization of the image for the user, such as content-based image retrieval. We propose an efficient similarity query algorithm with access control based on Bkd-tree in this paper, in which a searchable encryption scheme is designed for similarity image retrieval, and the encrypted image is used to extract image features by a pre-trained CNN model. The Bkd-tree is utilized to generate an index tree for the image features to speed up retrieval and make it faster than linear indexing. Finally, the security performances of the proposed scheme is analyzed and the performance of this scheme is evaluated by experiments. The results show that the security of the image content and image features can be ensured, and it has a shorter retrieval time and higher retrieval efficiency.

A High-Precision Identification Method for Maize Leaf Diseases and Pests Based on LFMNet under Complex Backgrounds.

Maize, as one of the most important crops in the world, faces severe challenges from various diseases and pests. The timely and accurate identification of maize leaf diseases and pests is of great significance for ensuring agricultural production. Currently, the identification of maize leaf diseases and pests faces two key challenges: (1) In the actual process of identifying leaf diseases and pests, complex backgrounds can interfere with the identification effect. (2) The subtle features of diseases and pests are difficult to accurately extract. To address these challenges, this study proposes a maize leaf disease and pest identification model called LFMNet. Firstly, the localized multi-scale inverted residual convolutional block (LMSB) is proposed to perform preliminary down-sampling on the image, preserving important feature information for the subsequent extraction of fine disease and pest features in the model structure. Then, the feature localization bottleneck (FLB) is proposed to improve the model's ability to focus on and locate disease and pest characteristics and to reduce interference from complex backgrounds. Subsequently, the multi-hop local-feature fusion architecture (MLFFA) is proposed, which effectively addresses the problem of extracting subtle features by enhancing the extraction and fusion of global and local disease and pest features in images. After training and testing on a dataset containing 19,451 images of maize leaf diseases and pests, the LFMNet model demonstrated excellent performance, with an average identification accuracy of 95.68%, a precision of 95.91%, a recall of 95.78%, and an F1 score of 95.83%. Compared to existing models, it exhibits significant advantages, offering robust technical support for the precise identification of maize diseases and pests.

Image features and clinical analysis of retroperitoneal pelvic schwannoma: a case report

BackgroundSchwannomas are benign usually encapsulated nerve sheath tumors derived from the Schwann cells, and affecting single or multiple nerves. The tumors commonly arise from the cranial nerves as acoustic neurinomas but they are extremely rare in the pelvis and the retroperitoneal area. Retroperitoneal pelvic schwannomas often present with non-specific symptoms leading to misdiagnosis and prolonged morbidity.Case presentationWe report the case of a 59-year-old woman presenting with a feeling of heaviness in the lower abdomen who was found to have a retroperitoneal pelvic schwannoma originating from the right femoral nerve. She had a history of two resections of peripheral schwannomas at four different sites of limbs. After conducting magnetic resonance imaging, this pelvic schwannoma was misdiagnosed as a gynecological malignancy. The tumor was successfully removed by laparoscopic surgery. Pathological analysis of the mass revealed a benign schwannoma of the femoral nerve sheath with demonstrating strong, diffuse positivity for S-100 protein.ConclusionsAlthough retroperitoneal pelvic schwannoma is rare, it should be considered in the differential diagnosis of pelvic masses, especially in patients with a history of neurogenic mass or the presence of neurogenic mass elsewhere.

Deep Residual Learning-Based Classification with Identification of Incorrect Predictions and Quantification of Cellularity and Nuclear Morphological Features in Digital Pathological Images of Common Astrocytic Tumors.

Interobserver variations in the pathology of common astrocytic tumors impact diagnosis and subsequent treatment decisions. This study leveraged a residual neural network-50 (ResNet-50) in digital pathological images of diffuse astrocytoma, anaplastic astrocytoma, and glioblastoma to recognize characteristic pathological features and perform classification at the patch and case levels with identification of incorrect predictions. In addition, cellularity and nuclear morphological features, including axis ratio, circularity, entropy, area, irregularity, and perimeter, were quantified via a hybrid task cascade (HTC) framework and compared between different characteristic pathological features with importance weighting. A total of 95 cases, including 15 cases of diffuse astrocytoma, 11 cases of anaplastic astrocytoma, and 69 cases of glioblastoma, were collected in Taiwan Hualien Tzu Chi Hospital from January 2000 to December 2021. The results revealed that an optimized ResNet-50 model could recognize characteristic pathological features at the patch level and assist in diagnosis at the case level with accuracies of 0.916 and 0.846, respectively. Incorrect predictions were mainly due to indistinguishable morphologic overlap between anaplastic astrocytoma and glioblastoma tumor cell area, zones of scant vascular lumen with compact endothelial cells in the glioblastoma microvascular proliferation area mimicking the glioblastoma tumor cell area, and certain regions in diffuse astrocytoma with too low cellularity being misrecognized as the glioblastoma necrosis area. Significant differences were observed in cellularity and each nuclear morphological feature among different characteristic pathological features. Furthermore, using the extreme gradient boosting (XGBoost) algorithm, we found that entropy was the most important feature for classification, followed by cellularity, area, circularity, axis ratio, perimeter, and irregularity. Identifying incorrect predictions provided valuable feedback to machine learning design to further enhance accuracy and reduce errors in classification. Moreover, quantifying cellularity and nuclear morphological features with importance weighting provided the basis for developing an innovative scoring system to achieve objective classification and precision diagnosis among common astrocytic tumors.

ALdamage-seg: A Lightweight Model for Instance Segmentation of Aluminum Profiles

Aluminum profiles are widely used in various manufacturing sectors due to their flexibility and chemical properties. However, these profiles are susceptible to defects during manufacturing and transportation. Detecting these defects is crucial, but existing object detection models like Mask R-CNN and YOLOv8-seg are not optimized for this task. These models are large and computationally intensive, making them unsuitable for edge devices used in industrial inspections. To address this issue, this study proposes a novel lightweight instance segmentation model called AL-damage-seg, inspired by the YOLOv8n-seg architecture. This model utilizes MobileNetV3 as the backbone. In YOLOv8n-seg, the role of C2f is to enhance the nonlinear representation of the model to capture complex image features more efficiently. We upgraded and improved it to form multilayer feature extraction module (MFEM) and integrates a large separable kernel attention (LSKA) mechanism in the C2f module, resulting in C2f_LSKA, to further optimize the performance of the model. Additionally, depth-wise separable convolutions are employed in the feature fusion process. The ALdamage-seg’s weight on the Alibaba Tian-chi aluminum profile dataset constitutes 43.9% of that of YOLOv8n-seg, with its GFLOPs reduced to 53% relative to YOLOv8-seg, all the while achieving an average precision (mAP) of 99% relative to YOLOv8-seg. With its compact size and lower computational requirements, this model is well-suited for deployment on edge devices with limited processing capabilities.

Language-vision matching for text-to-image synthesis with context-aware GAN

Text-to-image generation (T2I) aims to produce visually compelling images while maintaining a high degree of semantic consistency with textual descriptions. Despite the impressive progress made by existing methods, there are problems with limited details of synthesized images and insufficient correlation between the provided text description and the generated images. To address these issues, we propose a Context-Aware Generative Adversarial Network (CA-GAN), which generates images aligned with the input text representations. Specifically, the Context-Aware Block (CA-Block) learns a semantic-adaptive transformation based on text style, enabling the effective fusion of text descriptions and image features for high-quality image generation with better language-vision matching. Furthermore, we propose an Attention Convolution Module (ACM) to identify greater representative traits and avoid the inability to capitalize on non-local contextual information, which enables our model to generate images that exhibit numerous detailed attributes while maintaining high-quality and semantic consistency. Thereafter, we integrate self-attention with convolution to enhance feature maps to reinforce the semantic information in the discriminator, emphasizing critical feature channels while suppressing extraneous information, ultimately yielding more detailed and richer images. The experimental results demonstrate the superiority of our method over the SOTA approaches. In addition, further studies confirm the effectiveness of the generated visual details, which exhibit a high degree of alignment with the input text descriptions. Notably, our attention mechanism showcases cooperative effects contributing to overall performance improvement. The code is available at: https://github.com/hylneu/CAGAN.

Image Feature Extraction Using Symbolic Data of Cumulative Distribution Functions

Symbolic data analysis is an emerging field in statistics with great potential to become a standard inferential technique. This research introduces a new approach to image feature extraction using the empirical cumulative distribution function (ECDF) and distribution function of distribution values (DFDV) as symbolic data. The main objective is to reduce the dimension of huge pixel data by organizing them into more coherent pixel-intensity distributions. We propose a partitioning method with different breakpoints to capture pixel intensity variations effectively. This results in an ECDF representing the proportion of pixel intensities and a DFDV representing the probability distribution at specific points. The novelty of this approach lies in using ECDF and DFDV as symbolic features, thus summarizing the data and providing a more informative representation of the pixel value distribution, facilitating image classification analysis based on intensity distribution. The experimental results underscore the potential of this method in distinguishing image characteristics among existing image classes. Image features extracted using this approach promise image classification analysis with more informative image representations. In addition, theoretical insights into the properties of DFDV distribution functions are gained.

O-004 Oocyte image assessment by novel artificial intelligence (AI) model predicts blastocyst PGT-A results, outperforming age as a predictor

Abstract Study question Can a non-invasive, image analysis artificial intelligence (AI) model be built to predict the likelihood that a mature oocyte will develop into a euploid blastocyst? Summary answer A novel AI model was developed to assess images of mature oocytes, predicting chromosomal ploidy status of the developed blastocyst with an AUC of 0.71. What is known already The genetic status of blastocysts greatly impacts implantation and cycle success; therefore, PGT-A has become widely adopted as routine to aid in blastocyst selection for transfer and aims to decrease the time to pregnancy. Most chromosomal abnormalities arising in a blastocyst embryo are of maternal meiotic origin–indicating that the oocyte may have valuable information to be harnessed at the earliest stage in the process. Gaining insights into the potential genetic status of a blastocyst at the oocyte stage in a non-invasive manner would provide critical information to the overall fertility cycle. Study design, size, duration This study included a labeled dataset—9,260 images of mature denuded oocytes, immediately post-ICSI—from 3 clinics (Canada, United States (US), Spain) with euploid or aneuploid trophectoderm biopsy results (excluding mosaic/inconclusive embryos, n = 980). The labeled dataset was split into training (60%), validation (20%), and test (20%). An unlabeled dataset was also included—34,709 oocyte images (2 clinics; Canada, US)—and utilized in training. An external dataset (n = 473) from a separate US clinic was assessed for model generalization. Participants/materials, setting, methods Fully-supervised and semi-supervised machine learning models were experimented to predict blastocyst PGT-A from oocyte images. The labeled data were utilized in both methods. The semi-supervised method integrates unlabeled data using the well-known Mixmatch algorithm—learning important features from augmented images (with pseudo labels) that were then utilized in the supervised phase to predict outcomes. Age was incorporated into the models, therefore, a model only using age to predict ploidy was developed to act as a comparison. Main results and the role of chance Following various experiments, the fully-supervised model displayed an AUC 0.61, sensitivity 0.41, specificity 0.76, and accuracy 0.62 on the test set. The semi-supervised model displayed a performance of AUC 0.71, sensitivity 0.63, specificity 0.67, and accuracy 0.65–notably higher AUC and balanced performance on the positive/negative classes. The semi-supervised method benefits from both labeled and augmented unlabeled data for enhanced learning efficacy. The model utilizing age only to predict ploidy was extremely imbalanced in performance on the test set with AUC 0.63, sensitivity 1, specificity 0, and accuracy 0.48, confirming the importance of image features. Performance of the semi-supervised model was assessed by clinic. On Clinic 1 (n = 364; 40% euploid) it displayed AUC 0.61, sensitivity 0.60, specificity 0.56, and accuracy 0.58; on Clinic 2 (n = 675; 52% euploid) it displayed AUC 0.64, sensitivity 0.56, specificity 0.65, and accuracy of 0.60; on Clinic 3 (n = 466; 50% euploid) it displayed significantly better performance with AUC 0.85, sensitivity 0.76, specificity 0.79, and accuracy 0.77–warranting further investigation into model generalizability. On the external dataset (n = 473, 58% euploid), the semi-supervised model displayed improved performance (AUC 0.62, sensitivity 0.61, specificity 0.61, accuracy 0.60) in comparison to the age only model (AUC 0.59, sensitivity 1, specificity 0, accuracy 0.58). Limitations, reasons for caution This dataset includes oocytes that developed into blastocysts and underwent PGT-A. Datasets including oocytes that did not reach the blastocyst stage must also be assessed to validate the model’s clinical utility. Despite utilizing diverse data, advancing the generalizability of the model requires additional investigations to ensure balanced performance across clinics. Wider implications of the findings Utilizing images of mature oocytes, an AI model predicts the ploidy status of resulting blastocysts, providing a powerful tool for assessing oocyte quality and genetic integrity. These preliminary findings are promising for those undergoing oocyte cryopreservation by striving to elucidate genetic potential at the earliest phase of the IVF process. Trial registration number Not applicable.