Abstract
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.
Abstract (translated)
尽管在医学影像分析中深度学习的成功已经让人印象深刻，但由于高质量 labeled 图像的稀缺性，医学图像分割仍然具有挑战性。此外，自然图像和医学图像以及超声图像之间显著的领域差距会阻碍将基于自然图像训练的模型用于当前任务的微调。在这项工作中，我们解决了在低数据 regime 下分割模型的性能下降问题，并提出了一个无需提示的分割方法，利用分割基础模型的能力对抽象形状进行分割。我们通过使用粗粒度语义分割掩码作为输入和零散提示可优化目标来实现这一目标。我们在超声图像数据集上展示了我们的方法。在小的多关节超声图像数据集上进行低数据 regime 实验，各种低数据 regime 实验都表明，随着训练集大小的减小，性能提高。
URL
https://arxiv.org/abs/2404.16325
