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Updated: Mar 25, 2026
High-definition Fourier Transform Infrared FT-IR Spectroscopic Imaging of Human Tissue Sections towards Improving Pathology
Published on: January 21, 2015
Fully unsupervised inter-individual IR spectral histology of paraffinized tissue sections of normal colon
Thi Nguyet Que Nguyen1,2, Pierre Jeannesson1,2, Audrey Groh3
1Université de Reims Champagne-Ardenne, Equipe MéDIAN-Biophotonique et Technologies pour la Santé, UFR de Pharmacie, Reims, France.
Insights
This study introduces automated multi-image spectral histology for objective colon tissue analysis. The new method combines joint Extended Multiplicative Signal Correction (EMSC) and joint K-Means (KM) clustering for accurate identification of histological structures.
Area of Science:
- Biomedical Engineering
- Histology
- Spectroscopy
Background:
- Label-free Fourier-transform infrared histology offers detailed spectral imaging of tissue sections.
- Current methods face limitations due to empirical cluster number selection and color heterogeneity.
- Pathologist annotation is crucial but subjective and time-consuming.
Purpose of the Study:
- To develop an automated, objective multi-image spectral histology method for colon tissue analysis.
- To overcome limitations of single-image analysis and subjective annotations.
- To accurately identify histological structures in both murine and human colon tissues.
Main Methods:
- Simultaneous analysis of multiple spectral images using joint Extended Multiplicative Signal Correction (EMSC) for deparaffinization.
- Automated joint K-Means (KM) clustering with hierarchical double application of Pakhira-Bandyopadhyay-Maulik (PBM) validity index.
- Application to normal murine (8 images) and human (72 images) colon tissues.
Main Results:
- Accurate identification of key murine and human colon histological structures, including crypts, mucus, lamina propria, and submucosa.
- The automated procedure demonstrated insensitivity to reference spectra.
- High sensitivity was observed for the paraffin model in joint EMSC.
Conclusions:
- Combining joint EMSC and joint KM clustering with double PBM application enables objective and automated batched multi-image spectral histology.
- This approach enhances the reliability and efficiency of spectral histology for tissue analysis.
- The method holds promise for reproducible histological assessments in research and diagnostics.
Abstract:
In label-free Fourier-transform infrared histology, spectral images are individually recorded from tissue sections, pre-processed and clustered. Each single resulting color-coded image is annotated by a pathologist to obtain the best possible match with tissue structures revealed after Hematoxylin-Eosin staining. However, the main limitations of this approach are the empirical choice of the number of clusters in unsupervised classification, and the marked color heterogeneity between the clustered spectral images. Here, using normal murine and human colon tissues, we developed an automatic multi-image spectral histology to simultaneously analyze a set of spectral images (8 images mice samples and 72 images human ones). This procedure consisted of a joint Extended Multiplicative Signal Correction (EMSC) to numerically deparaffinize the tissue sections, followed by an automated joint K-Means (KM) clustering using the hierarchical double application of Pakhira-Bandyopadhyay-Maulik (PBM) validity index. Using this procedure, the main murine and human colon histological structures were correctly identified at both the intra- and the inter-individual levels, especially the crypts, secreted mucus, lamina propria and submucosa. Here, we show that batched multi-image spectral histology procedure is insensitive to the reference spectrum but highly sensitive to the paraffin model of joint EMSC. In conclusion, combining joint EMSC and joint KM clustering by double PBM application allows to achieve objective and automated batched multi-image spectral histology.
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