SovaSeg-Net: Scale Invariant Ovarian Tumors Segmentation from Ultrasound Images

Abstract

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Ovarian tumors are becoming a significant health concern for women worldwide, requiring accurate and effective diagnostic tools for early detection and treatment. This paper presents a new method to segment ovarian tumors from ultrasound images, aiming to reduce the healthcare burden and minimize the risk of oversight by less experienced medical professionals. This method is called SovaSeg-Net, built from the encoder-decoder deep learning architecture. The encoder leverages a convolutional neural network combined with a self attention module to extract meaningful features of tumors. It was then enhanced by the SPPF to combine features from different scales, increasing the encoder’s robustness to variation in scale, shape, and deformation of tumors. We also introduce a Joint Loss function that combines conventional IoU loss with focal loss and structural similarity loss to address data imbalance issues as well as the specific properties of ovarian tumors and ultrasound images. Experiments conducted on the benchmark OTU_2D, show that the proposed method outperforms existing methods, mainly in its ability to segment small tumors. This segmentation of ovarian tumors provides essential input for subsequent analysis steps facilitating the classification of tumors according to the rules established by the IOTA group. Source code is available at https://github.com/SonBH0410/SovaSeg-Net.