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1. Anatomical landmark segmentation in uterine cervix images using deep learning.
For automated evaluation of changes on uterine cervix, the external os (here simply os) is a primary anatomical landmark in locating the transformation zone (T-zone). Any abnormal tissue changes typically occur at or within the T-zone. This makes localizing the os on cervical images of great interest for detecting and classifying changes. However, there has been very limited work reported on segmentation of the os region in digitized cervix images, and to our knowledge no work has been done on sets of cervix images acquired from independent data collections exhibiting variabilities due to collection devices, environments, and procedures. In this paper, we present a process pipeline which consists of deep learning os region segmentation over such multiple datasets, followed by comprehensive evaluation of the performance. First, we evaluate of two state-of-the-art deep learning-based localization and classification algorithms, viz., Mask R-CNN and MaskX R-CNN, on multiple datasets. Second, in consideration of the os being small and irregularly-shaped, and of the variabilities in image quality, we use performance measurements beyond the commonly used DICE/IoU scores. We obtain higher performance, on a larger dataset, as compared with the work reported in the literature, and achieve a highest detection rate of 99.1% and an average minimal distance of 1.02 pixels. Furthermore, the network models we obtained in this study show potential use of quality control for data acquisition.