质谱成像
人工智能
马尔迪成像
化学
管道(软件)
仿射变换
插值(计算机图形学)
图像配准
H&E染色
转化(遗传学)
模式识别(心理学)
计算机视觉
质谱法
计算机科学
图像(数学)
基质辅助激光解吸/电离
病理
染色
医学
生物化学
数学
有机化学
色谱法
吸附
解吸
纯数学
基因
程序设计语言
作者
Weiwei Tang,Zhen Li,Yuchen Zou,Jun Liao,Bin Li
标识
DOI:10.1016/j.aca.2023.341969
摘要
The integration of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) and histology plays a pivotal role in advancing our understanding of complex heterogeneous tissues, which provides a comprehensive description of biological tissue with both wide molecule coverage and high lateral resolution. Herein, we proposed a novel strategy for the correction and registration of MALDI MSI data with hematoxylin & eosin (H&E) staining images. To overcome the challenges of discrepancies in spatial resolution towards the unification of the two imaging modalities, a deep learning-based interpolation algorithm for MALDI MSI data was constructed, which enables spatial coherence and the following orientation matching between images. Coupled with the affine transformation (AT) and the subsequent moving least squares algorithm, the two types of images from one rat brain tissue section were aligned automatically with high accuracy. Moreover, we demonstrated the practicality of the developed pipeline by projecting it to a rat cerebral ischemia-reperfusion injury model, which would help decipher the link between molecular metabolism and pathological interpretation towards microregion. This new approach offers the chance for other types of bioimaging to boost the field of multimodal image fusion.
科研通智能强力驱动
Strongly Powered by AbleSci AI