BreastSAM: A Study of Segment Anything Model for Breast Tumor Detection
  in Ultrasound Images
Mingzhe Hu Yuheng Li Xiaofeng Yang
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.