AIMS: This study investigated the feasibility of automated differentiation between essential tissue types encountered during laparoscopic colorectal surgery using spectral analysis. METHODS: Wide band (440-1830 nm) spectra were collected using an optical fiber probe and spectrometer from freshly explanted, ex vivo, human colonic specimens. These data were normalized at 810 nm (an isobestic wavelength for hemoglobin and oxy-hemoglobin) and mathematically analyzed using total principal component regression (TPCR). RESULTS: 929 spectra were collected from specimens of 19 patients, distinguishing 5 tissue types: mesenteric fat (MF, n=269), blood vessels (BV, n=377), colonic tissue (CT, n=213), ureter (UR, n=10) and tumorous tissue in colon (TT, n=60). For each individual tissue type the distinctive ability was determined against all other tissue types pooled as a group. Paired probability density function (PDF) of "tissue" (centered around label 1) versus "all other pooled tissues" (centered around label 0) and the cumulative distribution function (CDF) at label crossover value 0.5 was determined for each tissue type (MF: CDF=0.99 [SD=0.19]; BV: CDF=0.95 [SD=0.29]; CT: CDF=0.98 [SD=0.22]; UR: CDF=0.99 [SD=0.09]; TT: CDF=0.99 [SD=0.18]). CONCLUSION: Automated spectral differentiation of blood vessel, ureter, mesenteric adipose tissue, colonic tissue and tumorous tissue in colon, is feasible in freshly explanted human colonic specimens. These results may be exploited for further steps toward multi- or hyperspectrally enhanced in vivo (laparoscopic) surgical imaging.
- Spectral analysis
- Automated tissue recognition
- Iatrogeniclatrogenic injury prevention
- Laparoscopic colorectal surgery