TY - JOUR
T1 - Characterization of ovarian tissue based on quantitative analysis of photoacoustic microscopy images
AU - Wang, Tianheng
AU - Yang, Yi
AU - Alqasemi, Umar
AU - Kumavor, Patrick D.
AU - Wang, Xiaohong
AU - Sanders, Melinda
AU - Brewer, Molly
AU - Zhu, Quing
PY - 2013/12/1
Y1 - 2013/12/1
N2 - In this paper, human ovarian tissue with malignant and benign features was imaged ex vivo using an optical-resolution photoacoustic microscopy (OR-PAM) system. The feasibility of PAM to differentiate malignant from normal ovarian tissues was explored by comparing the PAM images morphologically. Based on the observed differences between PAM images of normal and malignant ovarian tissues in microvasculature features and distributions, seven features were quantitatively extracted from the PAM images, and a logistic model was used to classify ovaries as normal or malignant. 106 PAM images from 18 ovaries were studied. 57 images were used to train the seven-parameter logistic model, and a specificity of 92.1% and a sensitivity of 89.5% were achieved; 49 images were then tested, and a specificity of 81.3% and a sensitivity of 88.2% were achieved. These preliminary results demonstrate the feasibility of our PAM system in mapping microvasculature networks as well as characterizing the ovarian tissue, and could be extremely valuable in assisting surgeons for in vivo evaluation of ovarian tissue during minimally invasive surgery.
AB - In this paper, human ovarian tissue with malignant and benign features was imaged ex vivo using an optical-resolution photoacoustic microscopy (OR-PAM) system. The feasibility of PAM to differentiate malignant from normal ovarian tissues was explored by comparing the PAM images morphologically. Based on the observed differences between PAM images of normal and malignant ovarian tissues in microvasculature features and distributions, seven features were quantitatively extracted from the PAM images, and a logistic model was used to classify ovaries as normal or malignant. 106 PAM images from 18 ovaries were studied. 57 images were used to train the seven-parameter logistic model, and a specificity of 92.1% and a sensitivity of 89.5% were achieved; 49 images were then tested, and a specificity of 81.3% and a sensitivity of 88.2% were achieved. These preliminary results demonstrate the feasibility of our PAM system in mapping microvasculature networks as well as characterizing the ovarian tissue, and could be extremely valuable in assisting surgeons for in vivo evaluation of ovarian tissue during minimally invasive surgery.
UR - https://www.scopus.com/pages/publications/84888812850
U2 - 10.1364/BOE.4.002763
DO - 10.1364/BOE.4.002763
M3 - Article
AN - SCOPUS:84888812850
SN - 2156-7085
VL - 4
SP - 2763
EP - 2768
JO - Biomedical Optics Express
JF - Biomedical Optics Express
IS - 12
ER -