BreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Images
This work addresses early breast cancer detection for women using ultrasound imaging, but it is incremental as it applies an existing model to a new medical domain with minor optimizations.
The study investigated the Segment Anything Model (SAM) for interactive segmentation of breast tumors in ultrasound images, finding that the ViT_l variant performed best with improved metrics when prompts were used, and it segmented both malignant and benign tumors effectively, though slightly better for benign ones.
Breast cancer is one of the most common cancers among women worldwide, with early detection significantly increasing survival rates. Ultrasound imaging is a critical diagnostic tool that aids in early detection by providing real-time imaging of the breast tissue. We conducted a thorough investigation of the Segment Anything Model (SAM) for the task of interactive segmentation of breast tumors in ultrasound images. We explored three pre-trained model variants: ViT_h, ViT_l, and ViT_b, among which ViT_l demonstrated superior performance in terms of mean pixel accuracy, Dice score, and IoU score. The significance of prompt interaction in improving the model's segmentation performance was also highlighted, with substantial improvements in performance metrics when prompts were incorporated. The study further evaluated the model's differential performance in segmenting malignant and benign breast tumors, with the model showing exceptional proficiency in both categories, albeit with slightly better performance for benign tumors. Furthermore, we analyzed the impacts of various breast tumor characteristics - size, contrast, aspect ratio, and complexity - on segmentation performance. Our findings reveal that tumor contrast and size positively impact the segmentation result, while complex boundaries pose challenges. The study provides valuable insights for using SAM as a robust and effective algorithm for breast tumor segmentation in ultrasound images.