Automatic Segmentation of Cervical Precancerous Lesions in Colposcopy Image Using Pyramid Scene Parsing Network and Transfer Learning

Cervical cancer is the second most common cancer among women worldwide.According to the 2020 estimates by GLOBOCAN in 185 countries, there were 604,000 new cases of cervical cancer and 342,000 deaths.In clinical practice, the segmentation of LSIL+ (cervical intraepithelial neoplasia+cervical cancer) lesions in colposcopic images (cervical imaging) is essential for assisting gynecologists in diagnosing cervical intraepithelial neoplasia grading and cervical cancer.It can also aid gynecologists in identifying the precise lesion area for further pathological examination.Existing computer-aided diagnosis algorithms exhibit poor segmentation performance due to insufficient training data that fail to focus on semantically meaningful lesion parts.In this study, we employed the improved Pyramid Scene Parsing Network (PSPNet-ResNet50) computer-aided diagnosis algorithm to automatically segment LSIL+ lesion areas in colposcopic images.We collected 971 images containing low-grade cervical intraepithelial neoplasia LSIL (CIN 1), high-grade cervical intraepithelial neoplasia HSIL (CIN 2/CIN 3), and cervical cancer from the Department of Obstetrics and Gynecology at Hebei University Affiliated Hospital.Two experienced gynecologists annotated the LSIL+ lesion areas to create a dataset for cervical lesion segmentation.We designed a lesion-aware convolution neural network transfer learning strategy to accomplish the lesion segmentation task.Comprehensive experiments were conducted to evaluate the proposed method's segmentation performance on clinical cervical images.Our research findings indicate that the PSPNet-ResNet50 network used in this study achieved the best segmentation results for automated (LSIL+ area) segmentation, with pixel accuracy (PA), mean pixel accuracy (MPA), precision (Pre), recall (Re), F1 score (F1), and mean intersection over union (MIoU) values of 95.21%, 89.83%, 84.58%, 82.22%, 83.38%, and 83.83%, respectively.