Efficient Oct Image Segmentation Using Neural Architecture Search (original) (raw)
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Υ-Net: A Spatiospectral Network for Retinal OCT Segmentation
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Automated segmentation of retinal optical coherence tomography (OCT) images has become an important recent direction in machine learning for medical applications. We hypothesize that the anatomic structure of layers and their high-frequency variation in OCT images make retinal OCT a fitting choice for extracting spectral domain features and combining them with spatial domain features. In this work, we present Υ-Net, an architecture that combines the frequency domain features with the image domain to improve the segmentation performance of OCT images. The results of this work demonstrate that the introduction of two branches, one for spectral and one for spatial domain features, brings very significant improvement in fluid segmentation performance and allows outperformance as compared to the well-known U-Net model. Our improvement was 13% on the fluid segmentation dice score and 1.9% on the average dice score. Finally, removing selected frequency ranges in the spectral domain demonst...
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Background and Objective: Optical coherence tomography (OCT) is a useful technique to monitor retinal layer state both in humans and animal models. Automated OCT analysis in rats is of great relevance to study possible toxic effect of drugs and other treatments before human trials. In this paper, two different approaches to detect the most significant retinal layers in a rat OCT image are presented. Methods: One approach is based on a combination of local horizontal intensity profiles along with a new proposed variant of watershed transformation and the other is built upon an encoder-decoder convolutional network architecture. Results: After a wide validation, an averaged absolute distance error of 3.77 ± 2.59 and 1.90 ± 0.91 μm is achieved by both approaches, respectively, on a batch of the rat OCT database. After a second test of the deep-learning-based method using an unseen batch of the database, an averaged absolute distance error of 2.67 ± 1.25 μm is obtained. The rat OCT database used in this paper is made publicly available to facilitate further comparisons.
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Optical coherence tomography (OCT) is a commonly-used method of extracting high resolution retinal information. Moreover there is an increasing demand for the automated retinal layer segmentation which facilitates the retinal disease diagnosis. In this paper, we propose a novel multi-prediction guided attention network (MPG-Net) for automated retinal layer segmentation in OCT images. The proposed method consists of two major steps to strengthen the discriminative power of a U-shape Fully convolutional network (FCN) for reliable automated segmentation. Firstly, the feature refinement module which adaptively re-weights the feature channels is exploited in the encoder to capture more informative features and discard information in irrelevant regions. Furthermore, we propose a multi-prediction guided attention mechanism which provides pixel-wise semantic prediction guidance to better recover the segmentation mask at each scale. This mechanism which transforms the deep supervision to sup...
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The quantification of intra-retinal boundaries in the Optical Coherence Tomography (OCT) is a crucial task to study and diagnose neurological and ocular diseases. Since the manual segmentation of layers is usually a time consuming task and relies on the user, an excessive volume of research has been done to do this job automatically and without interference of the user. Although, generally the same procedure is applied to extract all layers, but finding the RNFL is typically more challenging due to the fact that it may vanish in some parts of the eye, especially close to the fovea. To have general software, besides using common methods such as applying the shortest path algorithm on the global gradient of an image, some extra steps have been taken here to narrow the search area for Dijstra's algorithm, especially for the second boundary. The result demonstrates high accuracy in segmenting the RNFL that is really important for the diagnosing Glaucoma.
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The accurate retina vessel segmentation (RVS) is of great significance to assist doctors in the diagnosis of ophthalmology diseases and other systemic diseases, and manually designing a valid neural network architecture for retinal vessel segmentation requires high expertise and a large workload. In order to further improve the performance of vessel segmentation and reduce the workload of manually designing neural network. We propose a specific search space based on encoder-decoder framework and apply neural architecture search (NAS) to retinal vessel segmentation. The search space is a macro-architecture search that involves some operations and adjustments to the entire network topology. For the architecture optimization, we adopt the modified evolutionary strategy which can evolve with limited computing resource to evolve the architectures. During the evolution, we select the elite architectures for the next generation evolution based on their performances. After the evolution, th...