Despite the remarkable success of deep learning in medical imaging analysis,
medical image segmentation remains challenging due to the scarcity of
high-quality labeled images for supervision. Further, the significant domain
gap between natural and medical images in general and ultrasound images in
particular hinders fine-tuning models trained on natural images to the task at
hand. In this work, we address the performance degradation of segmentation
models in low-data regimes and propose a prompt-less segmentation method
harnessing the ability of segmentation foundation models to segment abstract
shapes. We do that via our novel prompt point generation algorithm which uses
coarse semantic segmentation masks as input and a zero-shot prompt-able
foundation model as an optimization target. We demonstrate our method on a
segmentation findings task (pathologic anomalies) in ultrasound images. Our
method's advantages are brought to light in varying degrees of low-data regime
experiments on a small-scale musculoskeletal ultrasound images dataset,
yielding a larger performance gain as the training set size decreases.

通过使用粗糙语义分割掩模作为输入以及零 - shot 可提示的基础模型作为优化目标，我们提出了一种不需要提示的分割方法来解决低数据环境下分割模型的性能下降问题，并在肌肉骨骼超声图像数据集上进行了实验证明我们方法的优势，在不同程度的低数据环境下，随着训练集大小的减小，取得了更大的性能提升。