Automated Spectroscopic Tissue Classification in Colorectal Surgery

Background: In colorectal surgery, detecting ureters and mesenteric arteries is of utmost importance to prevent iatrogenic injury and to facilitate intraoperative decision making. A tool enabling ureter- and artery-specific image enhancement within (and possibly through) surrounding adipose tissue would facilitate this need, especially during laparoscopy. To evaluate the potential of hyperspectral imaging in colorectal surgery, we explored spectral tissue signatures using single-spot diffuse reflectance spectroscopy (DRS). As hyperspectral cameras with silicon (Si) and indium gallium arsenide (InGaAs) sensor chips are becoming available, we investigated spectral distinctive features for both sensor ranges.

Methods: In vivo wide-band (wavelength range 350-1830 nm) DRS was performed during open colorectal surgery. From the recorded spectra, 36 features were extracted at predefined wavelengths: 18 gradients and 18 amplitude differences. For classification of respectively ureter and artery in relation to surrounding adipose tissue, the best distinctive feature was selected using binary logistic regression for Si- and InGaAs-sensor spectral ranges separately. Classification performance was evaluated by leave-one-out cross-validation.

Results: In 10 consecutive patients, 253 spectra were recorded on 53 tissue sites (including colon, adipose tissue, muscle, artery, vein, ureter). Classification of ureter versus adipose tissue revealed accuracy of 100% for both Si range and InGaAs range. Classification of artery versus surrounding adipose tissue revealed accuracies of 95% (Si) and 89% (InGaAs).

Conclusions: Intraoperative DRS showed that Si and InGaAs sensors are equally suited for automated classification of ureter versus surrounding adipose tissue. Si sensors seem better suited for classifying artery versus mesenteric adipose tissue. Progress toward hyperspectral imaging within this field is promising.