Ovarian Tumor Ultrasound Image Segmentation with Deep Neural Networks

The precise and automated segmentation of ovarian tumors in medical images plays a pivotal role in the treatment of ovarian cancer in women. U-Net has demonstrated remarkable success in the field of medical image segmentation. However, due to its small receptive field, U-Net faces challenges in extracting global context information. Moreover, due to the significant variation in scale and size among tumors, it is essential to employ a network capable of effectively extracting information at Multiple scales. In this study, we present a U-Net-based network named PCU-Net for the segmentation of ovarian tumors, incorporating ConvMixer and Pyramid Dilated Convolution (PDC) modules. The ConvMixer module captures global context information by utilizing large-size kernels. The PDC module integrates local and global contextual patterns through utilization of parallel dilated convolution with different dilation rate. Furthermore, our model has fewer parameters than U-Net. We assess the proposed method's performance using the Multi-Modality Ovarian Tumor Ultrasound (MMOTU) dataset. The results indicate that in comparison to U-Net, our proposed PCU-Net exhibits an improvement of 4.23% in terms of Intersection over Union (IoU) and 2.99% in terms of Dice Similarity Coefficient (DSC).