Structured layer surface segmentation for retina OCT using fully convolutional regression networks

He, Yufan; Carass, Aaron; Liu, Yihao; Jedynak, Bruno; Solomon, Sharon D.; Saidha, Shiv; Calabresi, Peter A.; Prince, Jerry L.

doi:10.1016/j.media.2020.101856

Cited by 74 publications

(80 citation statements)

References 30 publications

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“…Hence, the other two-thirds of EZ shortening could be due to the imbalanced training data set for the EZ line and OS area. In the future, in addition to including more training data for EZ, we will also consider incorporating the methods such as the one proposed by He et al 38 for nonpixelized segmentation to obtain a smooth and continuous retinal boundary.…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Model Composed of Two Convolutional Neural Networks (CNNs) for Automatic Retinal Layer Segmentation of OCT Images in Retinitis Pigmentosa (RP)

Wang

Birch

2021

Trans. Vis. Sci. Tech.

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Section: Discussionmentioning

confidence: 99%

A Hybrid Model Composed of Two Convolutional Neural Networks (CNNs) for Automatic Retinal Layer Segmentation of OCT Images in Retinitis Pigmentosa (RP)

Wang

Birch

2021

Trans. Vis. Sci. Tech.

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“…For retinal surface segmentation, the MAD between predicted and ground truth surface positions is used. To compare the cross-B-scan continuity of the surfaces segmented by different methods, inspired by [12], we calculate the surface distance between adjacent B-Scans as the statistics of flatness and plot the histogram for inspection.…”

Section: Methodsmentioning

confidence: 99%

Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images

Liu,

Wei,

et al. 2022

Preprint

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Automated surface segmentation of retinal layer is important and challenging in analyzing optical coherence tomography (OCT). Recently, many deep learning based methods have been developed for this task and yield remarkable performance. However, due to large spatial gap and potential mismatch between the B-scans of OCT data, all of them are based on 2D segmentation of individual B-scans, which may loss the continuity information across the B-scans. In addition, 3D surface of the retina layers can provide more diagnostic information, which is crucial in quantitative image analysis. In this study, a novel framework based on hybrid 2D-3D convolutional neural networks (CNNs) is proposed to obtain continuous 3D retinal layer surfaces from OCT. The 2D features of individual B-scans are extracted by an encoder consisting of 2D convolutions. These 2D features are then used to produce the alignment displacement field and layer segmentation by two 3D decoders, which are coupled via a spatial transformer module. The entire framework is trained end-to-end. To the best of our knowledge, this is the first study that attempts 3D retinal layer segmentation in volumetric OCT images based on CNNs. Experiments on a publicly available dataset show that our framework achieves superior results to state-of-the-art 2D methods in terms of both layer segmentation accuracy and cross-B-scan 3D continuity, thus offering more clinical values than previous works.

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“…This makes OCT one of the most common medical imaging techniques when studying relevant retinal structures and diseases. In particular, OCT has successfully been used to study nervous tissue thickness [5] and angle-closure assessment [6] in glaucoma; to diagnose Age-Related Macular Degeneration (AMD or ARMD) [7], [8] and Diabetic Macular Edema (DME) [9], [10]; to assess the size of the optic nerve [11]; and to visualise the retinal layers [12], [13] and the retinal vasculature [14], [15], among others. Its ability to create in vivo visualisations of pathological structures that may be present in the retina makes it a valuable imaging technique for early diagnostic and treatment of a wide range of relevant eye diseases.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation and Intuitive Visualisation of the Epiretinal Membrane in 3D OCT Images Using Deep Convolutional Approaches

et al. 2021

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Epiretinal Membrane (ERM) is a disease caused by a thin layer of scar tissue that is formed on the surface of the retina. When this membrane appears over the macula, it can cause distorted or blurred vision. Although normally idiopathic, its presence can also be indicative of other pathologies such as diabetic macular edema or vitreous haemorrhage. ERM removal surgery can preserve more visual acuity the earlier it is performed. For this purpose, we present a fully automatic segmentation system that can help the clinicians to determine the ERM presence and location over the eye fundus using 3D Optical Coherence Tomography (OCT) volumes. The proposed system uses a convolutional neural network architecture to classify patches of the retina surface. All the 2D OCT slices of the 3D OCT volume of a patient are combined to produce an intuitive colour map over the 2D fundus reconstruction, providing a visual representation of the presence of ERM which therefore facilitates the diagnosis and treatment of this relevant eye disease. A total of 2.428 2D OCT slices obtained from 20 OCT 3D volumes was used in this work. To validate the designed methodology, several representative experiments were performed. We obtained satisfactory results with a Dice Coefficient of 0.826 ± 0.112 and a Jaccard Index of 0.714 ± 0.155, proving its applicability for diagnosis purposes. The proposed system also demonstrated its simplicity and competitive performance with respect to other state-of-the-art approaches.

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Structured layer surface segmentation for retina OCT using fully convolutional regression networks

Cited by 74 publications

References 30 publications

A Hybrid Model Composed of Two Convolutional Neural Networks (CNNs) for Automatic Retinal Layer Segmentation of OCT Images in Retinitis Pigmentosa (RP)

A Hybrid Model Composed of Two Convolutional Neural Networks (CNNs) for Automatic Retinal Layer Segmentation of OCT Images in Retinitis Pigmentosa (RP)

Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images

Automatic Segmentation and Intuitive Visualisation of the Epiretinal Membrane in 3D OCT Images Using Deep Convolutional Approaches

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