Assessing the Performance of Foundation Models in Prostate Segmentation Across Different Ultrasound Modalities
Vivian S. Nguyen, Amoon Jamzad, Paul Wilson, Mahdi Gilany, Purang Abolmaesumi, Michael Leveridge, Robert Siemens, Parvin Mousavi · 2024
Prostate cancer (PCa) remains a major health issue for men and relies on early diagnostic procedures such as transrectal ultrasound-guided (TRUS) biopsy for effective treatment. Since this procedure is prone to high rates of false negatives, advancements are being made in ultrasound (US) imaging and artificial intelligence to improve the detection of cancerous lesions. Segmentation is necessary for establishing the area for analysis however, manual segmentation can be laborious and time-intensive. Recent studies have focused on leveraging pre-trained foundation models that can be applied to various downstream tasks such as prostate segmentation in US imaging. However, only the processed B-mode US images were used in these studies and other imaging modes like raw RF images, although proved superior in tissue characterization, have not been explored. This study evaluates the segmentation capabilities of SAM-Med2D, a foundation model finetuned on a large-scale medical dataset using different ultrasound modalities. We finetune the pre-trained model (baseline) with and without adapter layers on prostate US data. Furthermore, we assess the results across different anatomical locations of the prostate. Our findings show that B-mode base data are more compatible with the SAM-Med2D model. Also, we observe a substantial improvement in model performance over baseline when finetuning with a small dataset, slightly better performance when employing adapter layers, and no substantial difference between anatomical locations. Overall, our work highlights the potential application of SAM-Med2D in various US modalities, which is essential for RF-based pipelines.