engleski

Segmentation of retinal structures in optical coherent tomography images

Optical coherence tomography (OCT) images of the retina provide a structural representation and give an insight into the pathological changes present in age-related macular degeneration (AMD). Due to the three-dimensionality and complexity of the images, manual analysis of pathological features is difficult, time-consuming, and prone to subjectivity. Computer analysis of 3D OCT images is necessary to enable automated quantitative measuring of the features, objectively and repeatedly. As supervised and semi-supervised learning-based automatic segmentation depends on the training data and quality of annotations, a new database of Annotated Retinal OCT Images (AROI database) has been created. It consists of 1136 images with annotations for pathological changes (fluid accumulation and related findings) and basic structures (layers) in patients with AMD. Inter- and intra-observer errors have been calculated to enable the validation of developed algorithms in relation to human variability. The framework for validation of OCT image segmentation methods consisting of a publicly available database of manually labeled image features and methodology and programming code for standardized measurement of segmentation accuracy is developed thus making the first scientific contribution. In the case of retinal OCT images of patients suffering from AMD, there is a significant deformation of the retinal structure. This causes the methods for retinal layers segmentation without introducing information on pathology significantly impaired. Therefore, an approach for layer segmentation which takes into account information about fluids (joint segmentation of layers and fluids) was chosen. Also, the automatic segmentation with standard U-net architecture and two state-of-the- art architectures for medical image segmentation has been performed to set a baseline for further algorithm development and to get insight into challenges for automatic segmentation. After an analysis of how various architectures and their modifications affect the performance and accuracy of segmentation, an analysis of the effects of various data manipulations (data augmentation, image post-processing) on segmentation performance was performed. Based on all the examined methods, comparison and proposal of two models for segmentation of retinal and intraretinal fluid layers in optical coherence tomography images that give comparable results were made. One model consists of a standard U-net architecture with post-processing using conditional random fields. The second model consists of a neural network architecture that combines the good features of the U-net architecture and the ResNet architecture. The existing state-of-the-art architecture has been adapted to the given problem and improved by adding skip connections. Additionally, segmentation accuracy was improved by using data augmentation and post-processing using conditional random fields. This is the second scientific contribution. A comparative quantitative analysis of fluid-only segmentation and fluid segmentation with available layer information was performed to gain insight into how much information about layers (fluid position in the retina) contributes to the accuracy of fluid segmentation itself. A study of the efficiency of transfer learning, which is an active and insufficiently researched topic in the field of deep learning with applications in medicine, was conducted, based on which a method for retinal fluid segmentation in optical coherence tomography images was proposed. This is the third scientific contribution. In conclusion, the main challenges of automatic segmentation are presented and the ideas for further research and improvements are given.

age-related macular degneration ; optical coherence tomography ; annotated retinal OCT images ; atumatic image segmentation ; deep learning

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