Segmentation of blood vessels in murine cerebral 3D OCTA images is
foundational for in vivo quantitative analysis of the effects of neurovascular
disorders, such as stroke or Alzheimer's, on the vascular network. However, to
accurately segment blood vessels with state-of-the-art deep learning methods, a
vast amount of voxel-level annotations is required. Since cerebral 3D OCTA
images are typically plagued by artifacts and generally have a low
signal-to-noise ratio, acquiring manual annotations poses an especially
cumbersome and time-consuming task. To alleviate the need for manual
annotations, we propose utilizing synthetic data to supervise segmentation
algorithms. To this end, we extract patches from vessel graphs and transform
them into synthetic cerebral 3D OCTA images paired with their matching ground
truth labels by simulating the most dominant 3D OCTA artifacts. In extensive
experiments, we demonstrate that our approach achieves competitive results,
enabling annotation-free blood vessel segmentation in cerebral 3D OCTA images.

使用合成数据来监督分割算法，提出了一种在小鼠脑部 3D OCTA 图像中实现无标记血管分割的方法。

脑部 3D OCTA 图像中基于模拟的血管分割

Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA  Images

Vessel structure is one of the most important parts of the retina which
physicians can detect many diseases by analysing its features. Localization of
blood vessels in retina images is an important process in medical image
analysis. This process is also more challenging with the presence of bright and
dark lesions. In this thesis, two automated vessel localization methods to
handle both healthy and unhealthy (pathological) retina images are analyzed.
Each method consists of two major steps and the second step is the same in the
two methods. In the first step, an algorithm is used to decrease the effect of
bright lesions. In Method 1, this algorithm is based on K- Means segmentation,
and in Method 2, it is based on a regularization procedure. In the second step
of both methods, a multi-scale line operator is used to localize the
line-shaped vascular structures and ignore the dark lesions which are generally
assumed to have irregular patterns. After the introduction of the methods, a
detailed quantitative and qualitative comparison of the methods with one
another as well as the state-of-the-art solutions in the literature based on
the segmentation results on the images of the two publicly available datasets,
DRIVE and STARE, is reported. The results demonstrate that the methods are
highly comparable with other solutions.

本文研究了两种自动化的血管定位方法，用于处理健康和不健康（病理性）视网膜图像，通过降低亮斑效应和使用多尺度线运算符定位线状血管结构来忽略黑斑病变。通过与现有解决方案的定量和定性比较，结果表明这些方法与其他解决方案高度可比。

视网膜图像中血管的自动定位

Automated Localization of Blood Vessels in Retinal Images

Automated segmentation of the blood vessels in 3D volumes is an essential
step for the quantitative diagnosis and treatment of many vascular diseases. 3D
vessel segmentation is being actively investigated in existing works, mostly in
deep learning approaches. However, training 3D deep networks requires large
amounts of manual 3D annotations from experts, which are laborious to obtain.
This is especially the case for 3D vessel segmentation, as vessels are sparse
yet spread out over many slices and disconnected when visualized in 2D slices.
In this work, we propose a novel method to segment the 3D peripancreatic
arteries solely from one annotated 2D projection per training image with depth
supervision. We perform extensive experiments on the segmentation of
peripancreatic arteries on 3D contrast-enhanced CT images and demonstrate how
well we capture the rich depth information from 2D projections. We demonstrate
that by annotating a single, randomly chosen projection for each training
sample, we obtain comparable performance to annotating multiple 2D projections,
thereby reducing the annotation effort. Furthermore, by mapping the 2D labels
to the 3D space using depth information and incorporating this into training,
we almost close the performance gap between 3D supervision and 2D supervision.
Our code is available at: this https URL

我们提出了一种新的方法，通过深度监督仅使用一个注释的 2D 投影来单独分割 3D 胰周动脉，从而减少注释工作量，并且通过将 2D 标签映射到 3D 空间并将其纳入训练，几乎消除了 3D 监督与 2D 监督之间的性能差距。

基于单个 2D 投影和深度监督的对比增强 CT 图像的 3D 动脉分割

3D Arterial Segmentation via Single 2D Projections and Depth Supervision  in Contrast-Enhanced CT Images

In this work, we utilize image segmentation to visually identify blood
vessels in retinal examination images. This process is typically carried out
manually. However, we can employ heuristic methods and machine learning to
automate or at least expedite the process. In this context, we propose a
cross-platform, open-source, and responsive software that allows users to
manually segment a retinal image. The purpose is to use the user-segmented
image to retrain machine learning algorithms, thereby enhancing future
automated segmentation results. Moreover, the software also incorporates and
applies certain image filters established in the literature to improve vessel
visualization. We propose the first solution of this kind in the literature.
This is the inaugural integrated software that embodies the aforementioned
attributes: open-source, responsive, and cross-platform. It offers a
comprehensive solution encompassing manual vessel segmentation, as well as the
automated execution of classification algorithms to refine predictive models.

使用图像分割技术在视网膜检查图像中自动识别血管，并通过机器学习方法改进算法准确度的跨平台开源软件。