## 4 Morphology of the Blastocyst and Quality Assessment
### Blastocyst Grading Approaches in IVF
#### 4.2.4 Machine Learning-Based Methods  
Advances in image processing not only help experts classifying blastocyst quality accurately [49], but also help in other applications such as segregating overlapping instances in microscopy images [50], developing annotation software [51], validating image analysis systems for blastocyst selection [52], standardizing embryo quality [40], predicting blastocyst development [53], etc. Furthermore, the emerging in AI with the application of image processing has seen several ART tools that have been developed to assist an embryologist in grading quality embryos such as ERICA [54], iDAScore v1.0 [55], CellProfiler 3.0 [56] and Blast-Net [57]. The capability of image processing methods to extract useful information from microscopic images such as texture, gray levels, and light levels [58] of blastocyst morphological parameters, are extensively significant for classifying specific blastocyst morphological features such as ZP [59; 60], ICM [61; 62] and TE [63]. In fact, automatic segmentation and quantification of various components of a blastocyst image can potentially offer automation in morphology assessment as well as embryo selection with improved implantation and live birth outcomes [49]. As an example, image processing protocols have been used to automatically extract different variables (up to 33 variables) that represent different aspect of the embryo and its expanding blastocyst characteristics [58] to assist embryologists in embryo selection for ART. Moreover, newly developed image processing algorithms such as 3D [56] and 4D [64] views will add more features to the embryo analysis tool. From the review, the blastocyst morphological segmentation and classification domains have been mostly dominated by semi-automated and fully-automated approaches. Most commonly, the segmentation and classification algorithms were embedded/fused in a deep learning-based model since the model architecture are containing features learning and classification stages [65]. Important information can be extracted from the regions of interest (ROI) in microscopic images by using a segmentation algorithm such as texture analysis [43; 50; 66; 67]. Among the most efficient texture analysis is grey level co-occurrence matrix (GLCM) [58], watershed transform [67] and k-means [67]. With the use of texture descriptor, each pixel of microscopic image can be characterized accordingly by assigning levels of each pixel in the blastocyst components as depicted in Figure 5. This map helps machine learning on identifying a few threshold values in finding the ROI of each pixel. The outmost ellipsoidal boundary represents ZP's inner boundary while \(R_{i}\) is the average of half major and minor axes of the inner ZP's calculated for each embryo individually. The outer most ellipsoidal tube (width of 0.1 \(Ri\), \(\Omega_{3}\)) represents the region that most definitely includes TE cells, whilst the inner most ellipse (\(\Omega_{1}\)) shows the region where ICM cells fully or partially reside in. The region that could include both TE and ICM is denoted as \(\Omega_{2}\). Figure 5: Biological characteristics of blastocyst cellular feature map and its component [67]. The application of image processing that uses mathematical techniques or clustering structures [68] to detect morphological texture for classification of TE, ICM and ZP components has been proved by [43; 59; 67; 69; 70]. In order to distinguish different textures, statistical measures of pixel values based on local standard deviation, \(\sigma\) and Entropy are computed as expressed in Equations (1) and (2), respectively. The \(\overline{x}\) refers to the mean pixel intensity over a neighborhood window of \(N\) (\(3\times 3\) pixels) and \(P_{i}\) is the probability of the \(i\)-th pixel value in the image.