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.

翻译：尽管深度学习在医学影像分析中取得了显著成功，但由于缺乏高质量标注图像进行监督，医学图像分割仍然面临挑战。此外，自然图像与医学图像（尤其是超声图像）之间存在显著的领域鸿沟，这阻碍了对基于自然图像训练的模型进行微调以适应具体任务。在本工作中，我们针对低数据场景下分割模型的性能退化问题，提出了一种无提示分割方法，该方法利用分割基础模型分割抽象形状的能力。我们通过新颖的提示点生成算法实现这一目标，该算法以粗语义分割掩膜作为输入，并以零样本可提示基础模型作为优化目标。我们在超声图像的分割发现任务（病理异常）上验证了该方法。在小型肌肉骨骼超声图像数据集上进行的低数据场景实验中，我们的方法在不同程度上展现出优势，且随着训练集规模的减小，性能增益显著增大。