AUTHOR=Chen Zhong , Zhang Changheng , Li Zhou , Yang Jinkun , Deng He 

TITLE=Automatic segmentation of ovarian follicles using deep neural network combined with edge information

JOURNAL=Frontiers in Reproductive Health

VOLUME=Volume 4 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/reproductive-health/articles/10.3389/frph.2022.877216

DOI=10.3389/frph.2022.877216

ISSN=2673-3153

ABSTRACT=Medical ultrasound image plays an important role in computer-aided diagnosis system. In many cases, it is the preferred method for doctors to diagnose. Combined with computer vision technology, segmentation of ovarian ultrasound images can help doctors accurately judge diseases, reduce doctors' workload and improve doctors' work efficiency. However, the accurate segmentation of ovarian ultrasound image is a challenging task. On the one hand, there is a lot of speckle noise in ultrasound images; on the other hand, the edges of objects are blurred in ultrasound images. In order to segment the target accurately, we propose an ovarian follicles segmentation network combined with edge information. By adding edge detection branch at the end of the network and taking the edge detection results as one of the losses of the network, we can accurately segment the ovarian follicles in ultrasound image, making the segmentation results more fine on the edge. Experiments show that the proposed network improves the segmentation accuracy of ovarian follicles, and has advantages over the current algorithm.