In this paper, we present SonoSAM - a promptable foundational model for
segmenting objects of interest on ultrasound images. Fine-tuned exclusively on
a rich, diverse set of objects from roughly 200k ultrasound image-mask pairs,
SonoSAM demonstrates state-of-the-art performance on 8 unseen ultrasound
data-sets, outperforming competing methods by a significant margin on all
metrics of interest. SonoSAM achieves average dice similarity score of more
than 90% on almost all test datasets within 2-6 clicks on an average, making it
a valuable tool for annotating ultrasound images. We also extend SonoSAM to 3-D
(2-D +t) applications and demonstrate superior performance making it a valuable
tool for generating dense annotations from ultrasound cine-loops. Further, to
increase practical utility of SonoSAM, we propose a two-step process of
fine-tuning followed by knowledge distillation to a smaller footprint model
without comprising the performance. We present detailed qualitative and
quantitative comparisons of SonoSAM with state-of-the art methods showcasing
efficacy of SonoSAM as one of the first reliable, generic foundational model
for ultrasound.

该论文介绍了 SonoSAM，一种用于超声图像的对象分割的可提示基础模型。SonoSAM 在经过微调的约 200,000 个超声图像 - 掩模对组成的丰富多样的对象集上表现出最先进的性能，在 8 个未见过的超声数据集上都显著优于竞争方法，并获得了超过 90％的平均 Dice 相似性分数，仅需 2-6 次点击即可进行标注，是一种用于注释超声图像的有价值的工具。作者还将 SonoSAM 扩展到了三维 (2D+t) 应用，并展示了出色的性能，使其成为从超声电影循环中生成密集注释的有价值工具。此外，为了提高 SonoSAM 的实用性，作者提出了一种先进行微调，然后通过知识蒸馏到较小的模型的两步过程，而不会影响性能。论文详细地定性和定量地比较了 SonoSAM 与最先进方法，展示了 SonoSAM 作为一种可靠的、通用的超声基础模型的功效。