An Optical Coherence Tomography Attenuation Compensation Algorithm Based on Adaptive Multi-Scale Retinex

Optical coherence tomography (OCT) is an optical high-resolution imaging technique for ophthalmic diagnosis. In this paper, we take advantages of multi-scale input, multi-scale side output and dual attention mechanism and present an enhanced nested U-Net architecture (MDAN-UNet), a new powerful fully convolutional network for automatic end-to-end segmentation of OCT images. We have evaluated two versions of MDAN-UNet (MDAN-UNet-16 and MDAN-UNet-32) on two publicly available benchmark datasets which are the Duke Diabetic Macular Edema (DME) dataset and the RETOUCH dataset, in comparison with other state-of-the-art segmentation methods. Our experiment demonstrates that MDAN-UNet-32 achieved the best performance, followed by MDAN-UNet-16 with smaller parameter, for multi-layer segmentation and multi-fluid segmentation respectively.

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Three Dimensional Data-Driven Multi Scale Atomic Representation of Optical Coherence Tomography

IEEE Transactions on Medical Imaging ◽

10.1109/tmi.2014.2374354 ◽

2015 ◽

Vol 34 (5) ◽

pp. 1042-1062 ◽

Cited By ~ 51

Author(s):

Raheleh Kafieh ◽

Hossein Rabbani ◽

Ivan Selesnick

Keyword(s):

Optical Coherence Tomography ◽

Three Dimensional ◽

Data Driven ◽

Optical Coherence ◽

Multi Scale ◽

Atomic Representation

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Group-wise context selection network for choroid segmentation in optical coherence tomography

Physics in Medicine and Biology ◽

10.1088/1361-6560/ac3a23 ◽

2021 ◽

Author(s):

Fei Shi ◽

Xuena Cheng ◽

Shuanglang Feng ◽

Changqing Yang ◽

Shengyong Diao ◽

...

Keyword(s):

Optical Coherence Tomography ◽

High Myopia ◽

State Of The Art ◽

Dice Similarity Coefficient ◽

Target Area ◽

Retinal Diseases ◽

Optical Coherence ◽

Multi Scale ◽

Select Group ◽

Boundary Optimization

Abstract Choroid thickness measured from optical coherence tomography (OCT) images has emerged as a vital metric in the management of retinal diseases such as high myopia. In this paper, we propose a novel group-wise context selection network (referred to as GCS-Net) to segment the choroid of either normal or high myopia eyes. To deal with the diverse choroid thickness and the variable shape of the pathological retina, GCS-Net adopts the group-wise channel dilation (GCD) module and the group-wise spatial dilation (GSD) module, which can automatically select group-wise multi-scale information under the guidance of channel attention or spatial attention, and enhance the consistency between the receptive field and the target area. Furthermore, a boundary optimization network with a new edge loss is incorporated to improve the resulting choroid boundary by deep supervision. Experimental results evaluated on a dataset composed of 1650 clinically obtained OCT B-scans show that the proposed GCS-Net can achieve a Dice similarity coefficient of 95.97±0.54%, which outperforms some state-of-the-art segmentation networks.

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