Automated segmentation of the ciliary muscle in OCT images using fully convolutional networks

Quantification of scleral changes during dynamic accommodation

The mechanics of accommodation is a complex process that involves multiple intraocular ocular structures. Recent studies suggest that there is deformation of the sclera during accommodation that may also play a role in accommodation, influencing ciliary muscle contraction and contributing to the accommodative response. However, the type and magnitude of the deformations measured varies significantly across studies. We present high-resolution synchronous OCT measurements of the anterior sclera contour and thickness and lens thickness acquired in real-time during accommodative responses to 4D step stimuli. The lens thickness was used as an assessment of objective accommodation. No changes in nasal and temporal anterior scleral contour and scleral thickness were found during accommodation within the precision of our measurements. Our results demonstrate that there are no significant scleral deformations during accommodation.

CMS-NET: deep learning algorithm to segment and quantify the ciliary muscle in swept-source optical coherence tomography images

Background

The ciliary muscle plays a role in changing the shape of the crystalline lens to maintain the clear retinal image during near work. Studying the dynamic changes of the ciliary muscle during accommodation is necessary for understanding the mechanism of presbyopia. Optical coherence tomography (OCT) has been frequently used to image the ciliary muscle and its changes during accommodation in vivo. However, the segmentation process is cumbersome and time-consuming due to the large image data sets and the impact of low imaging quality.

Objectives

This study aimed to establish a fully automatic method for segmenting and quantifying the ciliary muscle on the basis of optical coherence tomography (OCT) images.

Design

A perspective cross-sectional study.

Methods

In this study, 3500 signed images were used to develop a deep learning system. A novel deep learning algorithm was created from the widely used U-net and a full-resolution residual network to realize automatic segmentation and quantification of the ciliary muscle. Finally, the algorithm-predicted results and manual annotation were compared.

Results

For segmentation performed by the system, the total mean pixel value difference (PVD) was 1.12, and the Dice coefficient, intersection over union (IoU), and sensitivity values were 93.8%, 88.7%, and 93.9%, respectively. The performance of the system was comparable with that of experienced specialists. The system could also successfully segment ciliary muscle images and quantify ciliary muscle thickness changes during accommodation.

Conclusion

We developed an automatic segmentation framework for the ciliary muscle that can be used to analyze the morphological parameters of the ciliary muscle and its dynamic changes during accommodation.

Performance of the Deep Neural Network Ciloctunet, Integrated with Open-Source Software for Ciliary Muscle Segmentation in Anterior Segment OCT Images, Is on Par with Experienced Examiners

Anterior segment optical coherence tomography (AS-OCT), being non-invasive and well-tolerated, is the method of choice for an in vivo investigation of ciliary muscle morphology and function. The analysis requires the segmentation of the ciliary muscle, which is, when performed manually, both time-consuming and prone to examiner bias. Here, we present a convolutional neural network trained for the automatic segmentation of the ciliary muscle in AS-OCT images. Ciloctunet is based on the Freiburg U-net and was trained and validated using 1244 manually segmented OCT images from two previous studies. An accuracy of 97.5% for the validation dataset was achieved. Ciloctunet's performance was evaluated by replicating the findings of a third study with 180 images as the test data. The replication demonstrated that Ciloctunet performed on par with two experienced examiners. The intersection-over-union index (0.84) of the ciliary muscle thickness profiles between Ciloctunet and an experienced examiner was the same as between the two examiners. The mean absolute error between the ciliary muscle thickness profiles of Ciloctunet and the two examiners (35.16 µm and 45.86 µm) was comparable to the one between the examiners (34.99 µm). A statistically significant effect of the segmentation type on the derived biometric parameters was found for the ciliary muscle area but not for the selective thickness reading ("perpendicular axis"). Both the inter-rater and the intra-rater reliability of Ciloctunet were good to excellent. Ciloctunet avoids time-consuming manual segmentation, thus enabling the analysis of large numbers of images of ample study cohorts while avoiding possible examiner biases. Ciloctunet is available as open-source.

Wavelet attention network for the segmentation of layer structures on OCT images

Automatic segmentation of layered tissue is critical for optical coherence tomography (OCT) image analysis. The development of deep learning techniques provides various solutions to this problem, while most existing methods suffer from topological errors such as outlier prediction and label disconnection. The channel attention mechanism is a powerful technique to address these problems due to its simplicity and robustness. However, it relies on global average pooling (GAP), which only calculates the lowest frequency component and leaves other potentially useful information unexplored. In this study, we use the discrete wavelet transform (DWT) to extract multi-spectral information and propose the wavelet attention network (WATNet) for tissue layer segmentation. The DWT-based attention mechanism enables multi-spectral analysis with no complex frequency-selection process and can be easily embedded to existing frameworks. Furthermore, the various wavelet bases make the WATNet adaptable to different tasks. Experiments on a self-collected esophageal dataset and two public retinal OCT dataset demonstrated that the WATNet achieved better performance compared to several widely used deep networks, confirming the advantages of the proposed method.