You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina 
Chapter Title:  inTroducTion 
1. Abstract of Cited Paper (BIB001):  In the context of biomedical imaging analysis and computer-assisted diagnosis, segmentation analysis is an intense field of research and development. The most difficult part of medical image analysis is the automated localization and delineation of structures of interest. Automated data evaluation is one way of enhancing the clinical utility of measurements. In particular, medical image segmentation extracts meaningful information and facilitate the display of this information in a clinically relevant way. A crucial role for automated information extraction in medical imaging usually involves the segmentation of regions of the image in order to quantify volumes and areas of interest of biological tissues for further diagnosis and localization of pathologies. Optical coherence tomography (OCT) is a powerful imaging modality used to image various aspects of biological tissues, such as structural information, blood flow, elastic parameters, change of polarization states and molecular content (Huang et al., 1991). OCT uses the principle of low coherence interferometry to generate two or three dimensional imaging of biological samples by obtaining high-resolution cross-sectional backscattering profiles. A variety of successful algorithms for computer-aided diagnosis by means of OCT image analysis are presented in the literature, but robust use in clinical practice is still a major challenge for ongoing research in OCT image analysis. There are, therefore, efforts being made to improve clinical decision making based on automated analysis of OCT data. Particularly, in ophthalmology, efforts have been made to characterize clinically important features, such as damage to the fovea and optic nerve, automatically. The transfer of image analysis models from algorithmic development into clinical application is currently the major bottleneck due to the complexity of the overall process. For example, the process to establish an application for OCT medical image analysis requires difficult and complex tasks that should considers the following actions: 1) to define the OCT image data structures representing relevant biomedical features and the algorithms determining a valid example for given image values, 2) to select meaningful values for all technical parameters of the image data structures and algorithms and, as a result, to configure such a method to operate on specific OCT clinical data, 3) to run the algorithm with the selected parameters to find the individual model instance that best explains the input image and 4) to validate the procedure to ensure a trustworthy result from an automated segmentation algorithm even if a gold standard is unavailable.

Literature Review:
Optical coherence tomography (OCT) is a recently established imaging technique to describe different information about the internal structures of an object and to image various aspects of biological tissues, such as structural information, blood flow, elastic parameters, change of polarization states, and molecular content. BIB001 In contrast to OCT technology development which has been a field of active research since 1991, OCT image segmentation has only been more fully explored during the last decade. Segmentation, however, remains one of the most difficult and at the same time most commonly required steps in OCT image analysis. No typical segmentation method exists that can be expected to work equally well for all tasks. BIB002 One of the most challenging problems in OCT image segmentation is designing a system to work properly in clinical applications. There is no doubt that algorithms and research projects work on a limited number of images with some determinate abnormalities (or even on normal subjects) and such limitations make them more appropriate for bench and not for the bedside. Moreover, OCT images are inherently noisy, thus often requiring the utilization of 3D contextual information. Furthermore, the structure of the using 3D graphs constructed from the 3D OCT volumetric images. Some details of algorithms representing each class will be discussed in more detail and concluding remarks will be provided to compare the efficiency of different methods in different datasets. It should be noted that intrinsic noisy structure of retinal OCTs (particularly in old OCTs and after diseases) makes simple edge detection algorithms unsuitable for this purpose and researchers have tried to develop new algorithms to overcome such problems.