Continual learning (CL) methods designed for natural image classification
often fail to reach basic quality standards for medical image segmentation.
Atlas-based segmentation, a well-established approach in medical imaging,
incorporates domain knowledge on the region of interest, leading to
semantically coherent predictions. This is especially promising for CL, as it
allows us to leverage structural information and strike an optimal balance
between model rigidity and plasticity over time. When combined with
privacy-preserving prototypes, this process offers the advantages of
rehearsal-based CL without compromising patient privacy. We propose Atlas
Replay, an atlas-based segmentation approach that uses prototypes to generate
high-quality segmentation masks through image registration that maintain
consistency even as the training distribution changes. We explore how our
proposed method performs compared to state-of-the-art CL methods in terms of
knowledge transferability across seven publicly available prostate segmentation
datasets. Prostate segmentation plays a vital role in diagnosing prostate
cancer, however, it poses challenges due to substantial anatomical variations,
benign structural differences in older age groups, and fluctuating acquisition
parameters. Our results show that Atlas Replay is both robust and generalizes
well to yet-unseen domains while being able to maintain knowledge, unlike
end-to-end segmentation methods. Our code base is available under
this https URL

提出了一种基于图谱回放的医学图像分割方法，利用原型生成高质量分割掩膜，通过图像配准在训练分布变化的同时保持一致性，并在七个公开的前列腺分割数据集上与现有的持续学习方法进行了比较，表明该方法既稳健又具有广泛的泛化能力，能够保持知识。

前列腺 MRI 的持续基于图像的分割

Continual atlas-based segmentation of prostate MRI

We introduce a one-shot segmentation method to alleviate the burden of manual
annotation for medical images. The main idea is to treat one-shot segmentation
as a classical atlas-based segmentation problem, where voxel-wise
correspondence from the atlas to the unlabelled data is learned. Subsequently,
segmentation label of the atlas can be transferred to the unlabelled data with
the learned correspondence. However, since ground truth correspondence between
images is usually unavailable, the learning system must be well-supervised to
avoid mode collapse and convergence failure. To overcome this difficulty, we
resort to the forward-backward consistency, which is widely used in
correspondence problems, and additionally learn the backward correspondences
from the warped atlases back to the original atlas. This cycle-correspondence
learning design enables a variety of extra, cycle-consistency-based supervision
signals to make the training process stable, while also boost the performance.
We demonstrate the superiority of our method over both deep learning-based
one-shot segmentation methods and a classical multi-atlas segmentation method
via thorough experiments.

本文提出了一种基于正反向一致性的循环对应学习设计，将一次性分割方法作为传统的基于图谱的分割问题来处理，从而使标签可以通过学习实现自动转移。该方法在医学图像分割中表现优异。