Layer segmentation is important to quantitative analysis of retinal optical
coherence tomography (OCT). Recently, deep learning based methods have been
developed to automate this task and yield remarkable performance. However, due
to the large spatial gap and potential mismatch between the B-scans of an OCT
volume, all of them were based on 2D segmentation of individual B-scans, which
may lose the continuity and diagnostic information of the retinal layers in 3D
space. Besides, most of these methods required dense annotation of the OCT
volumes, which is labor-intensive and expertise-demanding. This work presents a
novel framework based on hybrid 2D-3D convolutional neural networks (CNNs) to
obtain continuous 3D retinal layer surfaces from OCT volumes, which works well
with both full and sparse annotations. 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 vectors and layer
segmentation by two 3D decoders coupled via a spatial transformer module. Two
losses are proposed to utilize the retinal layers' natural property of being
smooth for B-scan alignment and layer segmentation, respectively, and are the
key to the semi-supervised learning with sparse annotation. The entire
framework is trained end-to-end. To the best of our knowledge, this is the
first work that attempts 3D retinal layer segmentation in volumetric OCT images
based on CNNs. Experiments on a synthetic dataset and three public clinical
datasets show that our framework can effectively align the B-scans for
potential motion correction, and achieves superior performance to
state-of-the-art 2D deep learning methods in terms of both layer segmentation
accuracy and cross-B-scan 3D continuity in both fully and semi-supervised
settings, thus offering more clinical values than previous works.

基于混合 2D-3D 卷积神经网络的创新框架可以从光学相干断层扫描 (OCT) 体积中获得连续的三维视网膜层面，并在全面和稀疏注释方面表现出卓越的性能，从而在体积的 OCT 图像中尝试三维视网膜层分割的首个工作。

利用混合的 2D-3D 网络实现视网膜 OCT 图像的三维连贯层分割的同时对齐和表面回归，具备全面和稀疏标注

Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D  Networks for 3D Coherent Layer Segmentation of Retinal OCT Images with Full  and Sparse Annotations

Modern biomedical image analysis using deep learning often encounters the
challenge of limited annotated data. To overcome this issue, deep generative
models can be employed to synthesize realistic biomedical images. In this
regard, we propose an image synthesis method that utilizes denoising diffusion
probabilistic models (DDPMs) to automatically generate retinal optical
coherence tomography (OCT) images. By providing rough layer sketches, the
trained DDPMs can generate realistic circumpapillary OCT images. We further
find that more accurate pseudo labels can be obtained through knowledge
adaptation, which greatly benefits the segmentation task. Through this, we
observe a consistent improvement in layer segmentation accuracy, which is
validated using various neural networks. Furthermore, we have discovered that a
layer segmentation model trained solely with synthesized images can achieve
comparable results to a model trained exclusively with real images. These
findings demonstrate the promising potential of DDPMs in reducing the need for
manual annotations of retinal OCT images.

利用深度学习的现代生物医学图像分析经常面临有限的注释数据的挑战，为了克服这个问题，可以使用深度生成模型来合成逼真的生物医学图像。通过提供粗略的层次草图，训练的去噪扩散概率模型（DDPM）可以生成逼真的近视视网膜光学相干断层扫描图像。此外，通过知识适应可以获得更准确的伪标签，对分割任务非常有益。通过这一方法，我们观察到层次分割准确性的持续改善，并通过各种神经网络验证。此外，我们还发现，仅使用合成图像训练的层次分割模型可以达到与仅使用真实图像训练的模型相当的结果。这些发现展示了 DDPM 在减少人工注释视网膜光学相干断层扫描图像的需求方面的潜在潜力。