BKSeg-Net: Segmentation of Ovarian Tumors from Ultrasound Images with Boundary Keypoints Loss

Thi-Loan Pham, Hoang-Son Bui, Van-Hung Le, Thi-Lan Le, Van-Thang Nguyen, Chi-Mai Pham, Hong-Thien Dang, Tuan-Anh Nguyen, Duy-Hai Vu, Hai Vu, Thanh-Hai Tran

Abstract: Automatic segmentation of ovarian tumors from ultrasound images plays a crucial role in assisting doctors in the diagnosis and decision-making process. However, this task still faces many challenges due to the characteristics of ultrasound images, particularly the unclear boundary of tumors with the background. This paper introduces a method for segmenting ovarian tumors namely called BKSeg-Net using deep learning. Our proposed method is built from a state-of-the-art encoder-decoder architecture (i.e. U-Net) with the integration of an attention gate to capture more correlated features. Besides, we introduce a novel loss function that integrates four distinct loss functions: Jaccard Loss, SSIM Loss, Focal Loss, and Keypoint Loss. While the first three losses are commonly used in the literature, our new component, Keypoint Loss, imposes additional constraints on the tumor boundaries, helping the model learn and predict the regions that better align with the actual tumors. The proposed method is validated on two benchmark datasets, OTU-2D and USOVA3D, demonstrating promising segmentation results with a sensitivity of 90.59% and 97.96% respectively.