A Method for Ovarian Tumor Segmentation based on Segment Anything Model

In ovarian cancer diagnosis and treatment, ultrasound is widely used as a screening method thanks to its low cost. Accurately segmenting ovarian tumors from ultrasound images is an important step for further investigation. However, due to the heterogeneity of tumors and the low quality of ultrasound images, the segmentation of ovarian tumors is a challenging task. This paper presents a method for ovarian tumor segmentation from ultrasound images based on Segment Anything Model (SAM). - a transformer based model trained on large-scale datasets. SAM has been proved to be effective for many segmentation tasks. However, SAM traditionally optimizes loss functions based on regions without considering structural similarity constraints between the actual and predicted regions. To enhance model learning, we incorporate IoU, SSIM, and Focal loss functions in SAM model. Furthermore, we utilize two prompts methods: manual prompts and automatic prompts based on the detection results of YOLOv5. Experimental results on OTU2D dataset show that the proposed method outperforms many state of the art methods and the baseline model with 95.12% of Precision when using manual prompts and 91.39% for automatic prompts. Additionally, to evaluate the generalization of the proposed method, we collect a new dataset named OvaTUS dataset and perform cross dataset evaluation.