Segmentation Of Neurosensory Retinal Detachment Related Fluid Based On Graph And Convolutional Neural Network

The healthcare sector is having a marvelous digital image data that has rich yet unexplored information.These data can be renovated into valuable knowledge that supports the physicians in the decision-making process.Ranging from manual segmentation assessment to fully automated,different methods have been proposed to analyze medical image data.As the visual-based and manual segmentation are time-consuming and prone to wrong interpretation,computer scientists have been devoted to developing automated approaches.The graph-theory and machine learning approaches are among the operative and widely used techniques to develop automated methods for medical image analysis.Convolutional neural network(CNN)is a subset of machine learning that works to get self-adaptive features through layered and connected algorithms where each algorithm provides a different interpretation of the data it feeds on.Graph-based models are flexible and powerful ways to represent various problems in terms of graph optimization.Though there are various types of graph-based methods,this work focuses on graph-search and graph-cut approaches.These approaches enable to integrate different smoothness constraints,such as regional and boundary information to the graph construction so the problem is solved in a manner that leads to a spatial smoothness to be answered.This research tried to investigate the competence of CNN and graph-based approaches on the identification of neurosensory retinal detachment(NRD)related fluid from spectral domain optical coherence tomography(SD-OCT)images.NRD is a relatively common cause of vision damage among male individuals in many developed countries.SD-OCT is a well-established imaging modality in ophthalmology diagnosis that enables instant and direct imaging of morphological retinal tissue.Regardless of its advantage,SD-OCT produces a relatively low signal-to-noise ratio that requires careful selection of analysis approaches.Given the severity of NRD and the nature of SD-OCT images,it is a predominantly decisive task to come up with a fully automated method that can support the early detection and follow-up of NRD to eliminate the possible blindness that would come in case of late diagnoses and wrong treatment.In order to contribute to the development of automated analysis techniques towards NRD related fluid from the SD-OCT image,in this study,the following tasks were conducted.1.A new method was developed to segment NRD related fluid and eleven retinal surfaces using graph-based approaches.One of the best achievements of this model is the introduction of a so-called ‘divide and merge' approach that addresses the computational expensiveness of graph-based optimization problems.On its way of addressing fluid and retinal layer segmentation,this model also presents a novel approach that locates the fovea region that catches different sized fovea regions without any information about the fovea radius.2.A convolutional neural network model was designed to address the segmentation of fluid-related abnormalities including the NRD related fluid,intra-retinal fluid(IRF)and pigment epithelial detachment(PED)from SD-OCT images.The model utilizes the upto-date CNN concepts to capture the anatomical difference of the fluid types so that it could precisely segment and categorize them.3.In its current status,CNN is highly subjected to hyperparameters selection and related architecture design to produce the best result.As vast and interdependent they are,it requires substantial work to choose appropriate hyperparameters.Other research areas such as sentence classification using CNN models have prior works that draw good guidelines to decide on best hyperparameters;however,it is not clear for tasks like fluid segmentation from SD-OCT images.Taking this into consideration,in this work extensive experiments were conducted to investigate and distinguish between important and comparatively inconsequential hyperparameters on the proposed model.The suggestions indicated in this work can be used as a starting choice for researchers involved in the future fluid segmentation task.The effect of each hyperparameter on the proposed model was evaluated in terms of accuracy,training time,testing time,and training stability during which was analyzed centering on training loss.4.Finally,both graph-based and CNN based models were tested on the same dataset to compare and reveal the strength and weaknesses of each approach.According to the result,in terms of accuracy,convolutional neural network models provided better results.The convolutional neural network model is scalable in that it can segment three types of retinal fluids without any change in the model setup.The success of CNN based models demands a significant amount of sample training datasets and computational resources such as GPU based computer systems.For the graph-based model,on another side,even if the model achieved lesser accuracy compared to CNN based model,it is computationally less expensive.The ‘divide and merge' approach significantly reduced the computational cost of graph optimization thus the model can run on any CPU based computer.Moreover,since the graphbased model depends on problem specification-based graph structure and image data,the model was able to segment NRD related fluid and eleven retinal surfaces using a prior information model represented via simple statistical information.The tasks like eleven retinal surfaces segmentation using the CNN model are currently not easily achievable.The reason behind this is that CNN demands large training datasets and traceability of all those layers via manual segmentation is relatively difficult on images from NRD affected eyes where the structure and intensity distribution of layers are significantly altered.The drawback of graph-based is that it requires application-specific transforms that include cost functions,constraints and model parameters which in some cases fail to represent datasets not diligently observed and represented by experts.For it to be clinically applicable,graph-based approaches demand experts' intervention,but for CNN the model can simply learn and update by itself if additional representative training dataset is available.