BreastSAM: adapting the segmentation anything model for breast tumor segmentation in ultrasound imaging

Mingzhe Hu; Yuheng Li; Xiaofeng Yang

doi:10.1117/12.3006773

1 April 2024 BreastSAM: adapting the segmentation anything model for breast tumor segmentation in ultrasound imaging

Mingzhe Hu, Yuheng Li, Xiaofeng Yang

Proceedings Volume 12932, Medical Imaging 2024: Ultrasonic Imaging and Tomography; 129320V (2024) https://doi.org/10.1117/12.3006773
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

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 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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingzhe Hu, Yuheng Li, and Xiaofeng Yang "BreastSAM: adapting the segmentation anything model for breast tumor segmentation in ultrasound imaging", Proc. SPIE 12932, Medical Imaging 2024: Ultrasonic Imaging and Tomography, 129320V (1 April 2024); https://doi.org/10.1117/12.3006773

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