## 2 State-of-the-Art Review  
Endometrial histologic dating criteria, such as thickness, glandular density, and maturation, are important in IVF, being crucial for the successful implantation of an embryo. These criteria are important predictors of the pregnancy outcome in IVF cycles. The endometrium is the innermost layer of the uterus and is the site of implantation of the fertilized egg during pregnancy. For the development of automated segmentation techniques for histologic images, several approaches merging computer vision and machine learning technologies have been put forth over the years. The automatic evaluation of histology images mainly depends on the target objects that have to be evaluated, such as cells, nuclei, glands, or tissue type. An automated segmentation algorithm based on morphology is first applied to light microscopy images stained with Hematoxylin and Eosin [19].  
Image segmentation is the process of locating certain objects in an image or splitting it into several areas, each of which represents a different object or portion of the image. For the purpose of segmenting or dividing up images, deep learning techniques such as autoencoders like UNet are utilized. Segmentation is based on the transformation of a high-dimensional space to a lower-dimensional space [20]. Usually, two-dimensional data are down-sampled to a one-dimensional vector and again decoded to its original appearance. Researchers proposed several types of autoencoders, which are applied for image segmentation tasks, including plain fully convolutional autoencoders, multi-loss autoencoders, fully convolutional variational autoencoders, and repeated fully convolutional autoencoders. Different autoencoders distinguish themselves with their own strengths and weaknesses. Therefore, the selection of a specific structure of the segmentation model depends on the task and image characteristics. [PERSON], with a group of researchers, proposed an assisting analysis tool that is used to examine the vascular network of bone histologic images. The proposed automatic method is based on a convolutional neural network (CNN) and random forest classifier (RFC) [21]. Novel segmentation methods are applied in the detection of individual nuclei in stained breast histopathology images [22] and segment whole biopsy slides [23]. The latter method employs a U-Net-like neural structure that was trained on a large annotated dataset. [PERSON] et al. proposed a multiscale attention learning network (MSAL-Net) to segment individual nuclei in histopathology images [24]. The proposed methods include mathematical morphology, convolutional neural networks, random forest classifiers, and multiscale attention learning networks. These methods have shown promising results in the automatic segmentation of histology images. Several variants of U-Net have been proposed, each with its own strengths and weaknesses. Many limitations can be overcome by combining different variants of U-Net models and their strengths.  
Our research investigates the potential of applying automatic histology image analysis for differentiating between receptive and nonreceptive endometrial samples using a metric-driven approach. Traditional assessment methods rely on a subjective evaluation by pathologists, introducing potential inconsistencies. Automatic image analysis offers an objective and quantitative alternative. This approach involves extracting the relevant metrics from histological images, which can then be used to distinguish between receptive and nonreceptive samples. This work explores the feasibility of this approach and paves the way for developing a reliable and automated system for endometrial receptivity assessment.