Your browser doesn't support javascript.
loading
iSegMSI: An Interactive Strategy to Improve Spatial Segmentation of Mass Spectrometry Imaging Data.
Guo, Lei; Liu, Xingxing; Zhao, Chao; Hu, Zhenxing; Xu, Xiangnan; Cheng, Kian-Kai; Zhou, Peng; Xiao, Yu; Shah, Mudassir; Xu, Jingjing; Dong, Jiyang; Cai, Zongwei.
Affiliation
  • Guo L; Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen361005, China.
  • Liu X; Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen361005, China.
  • Zhao C; Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen518055, China.
  • Hu Z; Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen361005, China.
  • Xu X; School of Mathematics and Statistics, The University of Sydney, Sydney, NSW2006, Australia.
  • Cheng KK; Innovation Centre in Agritechnology, Universiti Teknologi Malaysia, Muar, Johor84600, Malaysia.
  • Zhou P; Department of Thyroid and Breast Surgery, Shenzhen Second People's Hospital, Shenzhen518025, China.
  • Xiao Y; Department of Thyroid and Breast Surgery, Shenzhen Second People's Hospital, Shenzhen518025, China.
  • Shah M; Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen361005, China.
  • Xu J; Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen361005, China.
  • Dong J; Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen361005, China.
  • Cai Z; State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong KongSAR999077, China.
Anal Chem ; 94(42): 14522-14529, 2022 10 25.
Article in En | MEDLINE | ID: mdl-36223650
ABSTRACT
Spatial segmentation is a critical procedure in mass spectrometry imaging (MSI)-based biochemical analysis. However, the commonly used unsupervised MSI segmentation methods may lead to inappropriate segmentation results as the MSI data is characterized by high dimensionality and low signal-to-noise ratio. This process can be improved by the incorporation of precise prior knowledge, which is hard to obtain in most cases. In this study, we show that the incorporation of partial or coarse prior knowledge from different sources such as reference images or biological knowledge may also help to improve MSI segmentation results. Here, we propose a novel interactive segmentation strategy for MSI data called iSegMSI, which incorporates prior information in the form of scribble-regularization of the unsupervised model to fine-tune the segmentation results. By using two typical MSI data sets (including a whole-body mouse fetus and human thyroid cancer), the present results demonstrate the effectiveness of the iSegMSI strategy in improving the MSI segmentations. Specifically, the method can be used to subdivide a region into several subregions specified by the user-defined scribbles or to merge several subregions into a single region. Additionally, these fine-tuned results are highly tolerant to the imprecision of the scribbles. Our results suggest that the proposed iSegMSI method may be an effective preprocessing strategy to facilitate the analysis of MSI data.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Image Processing, Computer-Assisted / Fetus Type of study: Diagnostic_studies Limits: Animals / Humans Language: En Journal: Anal Chem Year: 2022 Document type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Image Processing, Computer-Assisted / Fetus Type of study: Diagnostic_studies Limits: Animals / Humans Language: En Journal: Anal Chem Year: 2022 Document type: Article Affiliation country: China