Skull-stripping is the removal of background and non-brain anatomical
features from brain images. While many skull-stripping tools exist, few target
pediatric populations. With the emergence of multi-institutional pediatric data
acquisition efforts to broaden the understanding of perinatal brain
development, it is essential to develop robust and well-tested tools ready for
the relevant data processing. However, the broad range of neuroanatomical
variation in the developing brain, combined with additional challenges such as
high motion levels, as well as shoulder and chest signal in the images, leaves
many adult-specific tools ill-suited for pediatric skull-stripping. Building on
an existing framework for robust and accurate skull-stripping, we propose
developmental SynthStrip (d-SynthStrip), a skull-stripping model tailored to
pediatric images. This framework exposes networks to highly variable images
synthesized from label maps. Our model substantially outperforms pediatric
baselines across scan types and age cohorts. In addition, the <1-minute runtime
of our tool compares favorably to the fastest baselines. We distribute our
model at this https URL

通过开发匹配儿科图像的头骨去除模型（d-SynthStrip），我们的方法在不同扫描类型和年龄群体中表现出色，同时运行时间短于最快基准模型。

提升儿童脑部图像的头颅剥离性能

Boosting Skull-Stripping Performance for Pediatric Brain Images

Unsupervised anomaly detection has gained significant attention in the field
of medical imaging due to its capability of relieving the costly pixel-level
annotation. To achieve this, modern approaches usually utilize generative
models to produce healthy references of the diseased images and then identify
the abnormalities by comparing the healthy references and the original diseased
images. Recently, diffusion models have exhibited promising potential for
unsupervised anomaly detection in medical images for their good mode coverage
and high sample quality. However, the intrinsic characteristics of the medical
images, e.g. the low contrast, and the intricate anatomical structure of the
human body make the reconstruction challenging. Besides, the global information
of medical images often remain underutilized. To address these two issues, we
propose a novel Masked Autoencoder-enhanced Diffusion Model (MAEDiff) for
unsupervised anomaly detection in brain images. The MAEDiff involves a
hierarchical patch partition. It generates healthy images by overlapping
upper-level patches and implements a mechanism based on the masked autoencoders
operating on the sub-level patches to enhance the condition on the unnoised
regions. Extensive experiments on data of tumors and multiple sclerosis lesions
demonstrate the effectiveness of our method.

我们提出了一种新颖的蒙版自编码器增强扩散模型 (MAEDiff)，用于无监督异常检测脑部图像。MAEDiff 包括分层补丁分割，通过重叠上层补丁生成健康图像，并在次级补丁上采用基于蒙版自编码器的机制来增强未加入噪声区域的条件。对肿瘤和多发性硬化症病灶的数据进行了大量实验证明了我们方法的有效性。

MAEDiff: 基于掩码自编码器增强扩散模型的无监督脑部图像异常检测

MAEDiff: Masked Autoencoder-enhanced Diffusion Models for Unsupervised  Anomaly Detection in Brain Images

Early diagnosis, playing an important role in preventing progress and
treating the Alzheimer's disease (AD), is based on classification of features
extracted from brain images. The features have to accurately capture main
AD-related variations of anatomical brain structures, such as, e.g., ventricles
size, hippocampus shape, cortical thickness, and brain volume. This paper
proposes to predict the AD with a deep 3D convolutional neural network
(3D-CNN), which can learn generic features capturing AD biomarkers and adapt to
different domain datasets. The 3D-CNN is built upon a 3D convolutional
autoencoder, which is pre-trained to capture anatomical shape variations in
structural brain MRI scans. Fully connected upper layers of the 3D-CNN are then
fine-tuned for each task-specific AD classification. Experiments on the
\emph{ADNI} MRI dataset with no skull-stripping preprocessing have shown our
3D-CNN outperforms several conventional classifiers by accuracy and robustness.
Abilities of the 3D-CNN to generalize the features learnt and adapt to other
domains have been validated on the \emph{CADDementia} dataset.

本文提出了一种基于 3D 卷积神经网络的方法，通过学习通用的 AD 生物标志物特征并适应不同领域的数据集，在没有经过颅骨去除的 MRI 数据集上，通过对解剖形态变异的捕捉，实现了 AD 的预测和分类，并在实验中验证了其优越性和鲁棒性。