Correlating neuropathology with neuroimaging findings provides a multiscale
view of pathologic changes in the human organ spanning the meso- to
micro-scales, and is an emerging methodology expected to shed light on numerous
disease states. To gain the most information from this multimodal, multiscale
approach, it is desirable to identify precisely where a histologic tissue
section was taken from within the organ in order to correlate with the tissue
features in exactly the same organ region. Histology-to-organ registration
poses an extra challenge, as any given histologic section can capture only a
small portion of a human organ. Making use of the capabilities of
state-of-the-art deep learning models, we unlock the potential to address and
solve such intricate challenges. Therefore, we create the ATOM benchmark
dataset, sourced from diverse institutions, with the primary objective of
transforming this challenge into a machine learning problem and delivering
outstanding outcomes that enlighten the biomedical community. The performance
of our RegisMCAN model demonstrates the potential of deep learning to
accurately predict where a subregion extracted from an organ image was obtained
from within the overall 3D volume. The code and dataset can be found at:
this https URL

利用深度学习模型，我们创建了 ATOM 基准数据集，旨在将组织学到器官的配准问题转化为机器学习问题，并提供引领生物医学界的卓越结果。我们的 RegisMCAN 模型的性能展示了深度学习准确预测从整体三维体积中提取的器官图像的子区域所在位置的潜力。

从组织平面到器官世界：基于深度共同注意网络的多模态生物医学图像配准基准数据集

From Tissue Plane to Organ World: A Benchmark Dataset for Multimodal  Biomedical Image Registration using Deep Co-Attention Networks

Ex vivo MRI of the brain provides remarkable advantages over in vivo MRI for
visualizing and characterizing detailed neuroanatomy, and helps to link
microscale histology studies with morphometric measurements. However, automated
segmentation methods for brain mapping in ex vivo MRI are not well developed,
primarily due to limited availability of labeled datasets, and heterogeneity in
scanner hardware and acquisition protocols. In this work, we present a high
resolution dataset of 37 ex vivo post-mortem human brain tissue specimens
scanned on a 7T whole-body MRI scanner. We developed a deep learning pipeline
to segment the cortical mantle by benchmarking the performance of nine deep
neural architectures. We then segment the four subcortical structures: caudate,
putamen, globus pallidus, and thalamus; white matter hyperintensities, and the
normal appearing white matter. We show excellent generalizing capabilities
across whole brain hemispheres in different specimens, and also on unseen
images acquired at different magnetic field strengths and different imaging
sequence. We then compute volumetric and localized cortical thickness
measurements across key regions, and link them with semi-quantitative
neuropathological ratings. Our code, containerized executables, and the
processed datasets are publicly available at:
this https URL.

本研究旨在解决自动化识别 ex vivo MRI 中人类大脑皮质和亚皮质结构、白质异常的问题，我们设计了一个基于深度学习的模型，基于高分辨率数据进行训练和测试，并结合脑病理学的定量评估，通过测量体积和局部皮层厚度来诊断和研究各种脑部疾病。

针对神经退行性疾病的结构病理相关定量分析的高分辨率 7T 体外 MRI 自动化深度学习分割

Automated deep learning segmentation of high-resolution 7 T ex vivo MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases