Synthesizing healthy brain scans from diseased brain scans offers a potential
solution to address the limitations of general-purpose algorithms, such as
tissue segmentation and brain extraction algorithms, which may not effectively
handle diseased images. We consider this a 3D inpainting task and investigate
the adaptation of 2D inpainting methods to meet the requirements of 3D magnetic
resonance imaging(MRI) data. Our contributions encompass potential
modifications tailored to MRI-specific needs, and we conducted evaluations of
multiple inpainting techniques using the BraTS2023 Inpainting datasets to
assess their efficacy and limitations.

从疾病大脑扫描中综合出健康脑扫描为解决一般算法的局限性，我们考虑了 3D 修复任务，并研究将 2D 修复方法调整为满足 3D 磁共振成像（MRI）数据要求的方法。我们的贡献包括针对 MRI 特定需求的潜在修改，并使用 BraTS2023 修复数据集对多个修复技术进行评估，以评估它们的效果和限制。

用生成模型推进脑部肿瘤修复

Advancing Brain Tumor Inpainting with Generative Models

Researchers in neuroscience have a growing number of datasets available to
study the brain, which is made possible by recent technological advances. Given
the extent to which the brain has been studied, there is also available
ontological knowledge encoding the current state of the art regarding its
different areas, activation patterns, key words associated with studies, etc.
Furthermore, there is an inherent uncertainty associated with brain scans
arising from the mapping between voxels -- 3D pixels -- and actual points in
different individual brains. Unfortunately, there is currently no unifying
framework for accessing such collections of rich heterogeneous data under
uncertainty, making it necessary for researchers to rely on ad hoc tools. In
particular, one major weakness of current tools that attempt to address this
kind of task is that only very limited propositional query languages have been
developed. In this paper, we present NeuroLang, an ontology language with
existential rules, probabilistic uncertainty, and built-in mechanisms to
guarantee tractable query answering over very large datasets. After presenting
the language and its general query answering architecture, we discuss
real-world use cases showing how NeuroLang can be applied to practical
scenarios for which current tools are inadequate.

本文介绍了一种具有存在规则、概率不确定性和内置机制的本体语言 ——NeuroLang，该语言可用于大型数据集查询，且适用于目前工具无法解决的实际情况和应用场景

基于不确定性的可扩展神经科学本体知识查询：神经语言学方法

Scalable Query Answering under Uncertainty to Neuroscientific Ontological Knowledge: The NeuroLang Approach

Deep learning-based methods have recently demonstrated promising results in
deformable image registration for a wide range of medical image analysis tasks.
However, existing deep learning-based methods are usually limited to small
deformation settings, and desirable properties of the transformation including
bijective mapping and topology preservation are often being ignored by these
approaches. In this paper, we propose a deep Laplacian Pyramid Image
Registration Network, which can solve the image registration optimization
problem in a coarse-to-fine fashion within the space of diffeomorphic maps.
Extensive quantitative and qualitative evaluations on two MR brain scan
datasets show that our method outperforms the existing methods by a significant
margin while maintaining desirable diffeomorphic properties and promising
registration speed.

本研究提出了一种基于深度学习的 Laplacian 金字塔图像注册网络，可以在差分同胚映射空间内以逐层逼近的方式解决图像配准优化问题，并在两个 MR 脑扫描数据集上得出了比现有方法更优越的结果，同时保持理想的差分同胚性和良好的配准速度。