Retinal Layer Segmentation Reformulated as OCT Language Processing

Tran, Arianne; Weiß, Jakob; Albarqouni, Shadi; Roohi, Shahrooz Faghih; Navab, Nassir

doi:10.1007/978-3-030-59722-1_67

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…We follow the same experimental protocol for training and evaluation of our method on the Duke OCT dataset, as in prior works [29,18]. The Duke OCT dataset consists of OCT scans from 10 patients, which are annotated by two experts.…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Using Recurrent Neural Networks (RNNs) for OCT segmentation has been explored in [14,29]. While [14] considered sequences between different scans, Tran et al [29] modeled OCT retinal layers using natural language and developed an OCT segmentation method using RNNs for processing pixel sequences. An autoencoder network with two encoder branches has been previously used for polyp detection [19] by taking advantage of a pre-trained VGG network.…”

Section: Related Workmentioning

confidence: 99%

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$Υ$-Net: A Spatiospectral Network for Retinal OCT Segmentation

Farshad¹,

Yeganeh²,

Gehlbach³

et al. 2022

Preprint

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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 demonstrates the impact of these features on the fluid segmentation outperformance.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

$Υ$-Net: A Spatiospectral Network for Retinal OCT Segmentation

Farshad¹,

Yeganeh²,

Gehlbach³

et al. 2022

Preprint

Self Cite

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“…Recent natural language processing (NLP) developments using recurrent neural networks (RNNs) have been explored for OCT segmentation. These models consider sequences between different scans for processing pixel sequences [37,38]. The work of [39] developed a polyp detection system using an autoencoder network with an encoder (pre-trained VGG19) and a decoder (U-Net) [28] branch to segment colon cancer cells.…”

Section: Related Workmentioning

confidence: 99%

Improving OCT Image Segmentation of Retinal Layers by Utilizing a Machine Learning Based Multistage System of Stacked Multiscale Encoders and Decoders

Sampath Kumar,

Schlosser,

Langner

et al. 2023

Bioengineering

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Optical coherence tomography (OCT)-based retinal imagery is often utilized to determine influential factors in patient progression and treatment, for which the retinal layers of the human eye are investigated to assess a patient’s health status and eyesight. In this contribution, we propose a machine learning (ML)-based multistage system of stacked multiscale encoders and decoders for the image segmentation of OCT imagery of the retinal layers to enable the following evaluation regarding the physiological and pathological states. Our proposed system’s results highlight its benefits compared to currently investigated approaches by combining commonly deployed methods from deep learning (DL) while utilizing deep neural networks (DNN). We conclude that by stacking multiple multiscale encoders and decoders, improved scores for the image segmentation task can be achieved. Our retinal-layer-based segmentation results in a final segmentation performance of up to 82.25±0.74% for the Sørensen–Dice coefficient, outperforming the current best single-stage model by 1.55% with a score of 80.70±0.20%, given the evaluated peripapillary OCT data set. Additionally, we provide results on the data sets Duke SD-OCT, Heidelberg, and UMN to illustrate our model’s performance on especially noisy data sets.

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