Automatic retinal layer segmentation with medical images, such as optical
coherence tomography (OCT) images, serves as an important tool for diagnosing
ophthalmic diseases. However, it is challenging to achieve accurate
segmentation due to low contrast and blood flow noises presented in the images.
In addition, the algorithm should be light-weight to be deployed for practical
clinical applications. Therefore, it is desired to design a light-weight
network with high performance for retinal layer segmentation. In this paper, we
propose LightReSeg for retinal layer segmentation which can be applied to OCT
images. Specifically, our approach follows an encoder-decoder structure, where
the encoder part employs multi-scale feature extraction and a Transformer block
for fully exploiting the semantic information of feature maps at all scales and
making the features have better global reasoning capabilities, while the
decoder part, we design a multi-scale asymmetric attention (MAA) module for
preserving the semantic information at each encoder scale. The experiments show
that our approach achieves a better segmentation performance compared to the
current state-of-the-art method TransUnet with 105.7M parameters on both our
collected dataset and two other public datasets, with only 3.3M parameters.

本文提出了一种轻量级网络，用于光学相干断层扫描图像的视网膜层分割，并通过多尺度特征提取和变换块对特征图的语义信息进行全面利用和全局推理能力的提升，并通过多尺度不对称注意（MAA）模块在每个编码器尺度上保留语义信息，实现了比当前最先进的 TransUnet 方法（具有 105.7M 参数）更好的分割效果，参数仅为 3.3M。

轻量级全局推理视网膜层分割

Light-weight Retinal Layer Segmentation with Global Reasoning

Domain shift is a major problem for deploying deep networks in clinical
practice. Network performance drops significantly with (target) images obtained
differently than its (source) training data. Due to a lack of target label
data, most work has focused on unsupervised domain adaptation (UDA). Current
UDA methods need both source and target data to train models which perform
image translation (harmonization) or learn domain-invariant features. However,
training a model for each target domain is time consuming and computationally
expensive, even infeasible when target domain data are scarce or source data
are unavailable due to data privacy. In this paper, we propose a novel self
domain adapted network (SDA-Net) that can rapidly adapt itself to a single test
subject at the testing stage, without using extra data or training a UDA model.
The SDA-Net consists of three parts: adaptors, task model, and auto-encoders.
The latter two are pre-trained offline on labeled source images. The task model
performs tasks like synthesis, segmentation, or classification, which may
suffer from the domain shift problem. At the testing stage, the adaptors are
trained to transform the input test image and features to reduce the domain
shift as measured by the auto-encoders, and thus perform domain adaptation. We
validated our method on retinal layer segmentation from different OCT scanners
and T1 to T2 synthesis with T1 from different MRI scanners and with different
imaging parameters. Results show that our SDA-Net, with a single test subject
and a short amount of time for self adaptation at the testing stage, can
achieve significant improvements.

提出了一种自适应网络方法，通过使用自适应器、任务模型和自编码器来解决深度网络在临床实践中的领域转移问题，本方法证明在使用单个测试对象和自适应的短时间内取得了显著改进，验证了方法的可行性并得到了广泛应用

自适应域网络

Self domain adapted network

Optical coherence tomography (OCT) is commonly used to analyze retinal layers
for assessment of ocular diseases. In this paper, we propose a method for
retinal layer segmentation and quantification of uncertainty based on Bayesian
deep learning. Our method not only performs end-to-end segmentation of retinal
layers, but also gives the pixel wise uncertainty measure of the segmentation
output. The generated uncertainty map can be used to identify erroneously
segmented image regions which is useful in downstream analysis. We have
validated our method on a dataset of 1487 images obtained from 15 subjects (OCT
volumes) and compared it against the state-of-the-art segmentation algorithms
that does not take uncertainty into account. The proposed uncertainty based
segmentation method results in comparable or improved performance, and most
importantly is more robust against noise.

该论文提出了一种基于贝叶斯深度学习的视网膜层分割和不确定性量化方法，其不仅可以进行视网膜层的端对端分割，而且还可以提供像素级的不确定性度量。通过生成的不确定性地图，可以识别出错误分割的图像区域，从而用于下游分析。该方法在视网膜图像分割方面具有可比或更好的性能，并且更加稳健抗噪声。