目次
ポスター発表
- 第3日 6月24日（金） P会場（501，502，503）
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3P-25 PDF
走査電子顕微鏡画像で作成した機械学習モデルを用いたマスイメージの画質改善
The matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-MSI) is used in various applications to investigate target compounds' localization. One of the issues of MALDI-MSI is the relatively low signal-to-noise ratio of extracted imaging data. It is due to the ununiform matrix application to the sample surface or low ion intensities from limited pixel regions. To solve the issue, we have developed the method to improve a quality of extracted mass imaging data using the machine learning model made by scanning electron microscope images. We have confirmed that the application of the machine learning model can improve the signal-to-noise ratio of extracted mass images.