Accurately annotated ultrasonic images are vital components of a high-quality
medical report. Hospitals often have strict guidelines on the types of
annotations that should appear on imaging results. However, manually inspecting
these images can be a cumbersome task. While a neural network could potentially
automate the process, training such a model typically requires a dataset of
paired input and target images, which in turn involves significant human
labour. This study introduces an automated approach for detecting annotations
in images. This is achieved by treating the annotations as noise, creating a
self-supervised pretext task and using a model trained under the Noise2Noise
scheme to restore the image to a clean state. We tested a variety of model
structures on the denoising task against different types of annotation,
including body marker annotation, radial line annotation, etc. Our results
demonstrate that most models trained under the Noise2Noise scheme outperformed
their counterparts trained with noisy-clean data pairs. The costumed U-Net
yielded the most optimal outcome on the body marker annotation dataset, with
high scores on segmentation precision and reconstruction similarity. We
released our code at this https URL

通过采用噪声到噪声（Noise2Noise）模型，在超声图像中自动检测标注物的研究中，我们发现噪声产生的模型比噪声 - 干净数据对训练的模型表现更好。其中，定制的 U-Net 模型在身体标记物标注数据集上表现出色，其分割精度和重建相似性得分较高。

超声图像标注去除：基于自监督 Noise2Noise 方法的研究

Ultrasonic Image's Annotation Removal: A Self-supervised Noise2Noise  Approach

The field of image denoising is currently dominated by discriminative deep
learning methods that are trained on pairs of noisy input and clean target
images. Recently it has been shown that such methods can also be trained
without clean targets. Instead, independent pairs of noisy images can be used,
in an approach known as Noise2Noise (N2N). Here, we introduce Noise2Void (N2V),
a training scheme that takes this idea one step further. It does not require
noisy image pairs, nor clean target images. Consequently, N2V allows us to
train directly on the body of data to be denoised and can therefore be applied
when other methods cannot. Especially interesting is the application to
biomedical image data, where the acquisition of training targets, clean or
noisy, is frequently not possible. We compare the performance of N2V to
approaches that have either clean target images and/or noisy image pairs
available. Intuitively, N2V cannot be expected to outperform methods that have
more information available during training. Still, we observe that the
denoising performance of Noise2Void drops in moderation and compares favorably
to training-free denoising methods.

介绍了一种名为 Noise2Void (N2V) 的图像降噪训练方案，可以不需要嘈杂和干净的图像对，而是直接使用被降噪的数据进 行训练，特别适用于生物医学图像数据。与具有噪声图像对和 / 或干净目标图像的方式相比，N2V 的降噪效果较为相当。