Affiliations: [a] Department of Information Engineering, Nanchang Hangkong University, Nanchang, Jiangxi 330036, China | [b] NuVasive, San Diego, California, CA 91355, USA | [c] Department of Gynecologic Oncology, Jiangxi Maternal and Child Health Hospital, Jiangxi 330006, China
Correspondence:
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Corresponding author: Hua Dong, Department of Information Engineering, Nanchang Hangkong University, Nanchang, Jiangxi 330036, China. Tel.: +86 18070502633; E-mail: [email protected].
Abstract: BACKGROUND: Acetowhite (AW) region is a critical physiological phenomenon of precancerous lesions of cervical cancer. An accurate segmentation of the AW region can provide a useful diagnostic tool for gynecologic oncologists in screening cervical cancers. Traditional approaches for the segmentation of AW regions relied heavily on manual or semi-automatic methods. OBJECTIVE: To automatically segment the AW regions from colposcope images. METHODS: First, the cervical region was extracted from the original colposcope images by k-means clustering algorithm. Second, a deep learning-based image semantic segmentation model named DeepLab V3+ was used to segment the AW region from the cervical image. RESULTS: The results showed that, compared to the fuzzy clustering segmentation algorithm and the level set segmentation algorithm, the new method proposed in this study achieved a mean Jaccard Index (JI) accuracy of 63.6% (improved by 27.9% and 27.5% respectively), a mean specificity of 94.9% (improved by 55.8% and 32.3% respectively) and a mean accuracy of 91.2% (improved by 38.6% and 26.4% respectively). A mean sensitivity of 78.2% was achieved by the proposed method, which was 17.4% and 10.1% lower respectively. Compared to the image semantic segmentation models U-Net and PSPNet, the proposed method yielded a higher mean JI accuracy, mean sensitivity and mean accuracy. CONCLUSION: The improved segmentation performance suggested that the proposed method may serve as a useful complimentary tool in screening cervical cancer.
Keywords: Cervical cancer, visual inspection with acetic acid, medical image segmentation, deep learning
