Segment Anything Model (SAM), a vision foundation model trained on large-scale annotations, has recently continued raising awareness within medical image segmentation. Despite the impressive capabilities of SAM on natural scenes, it struggles with performance decline when confronted with medical images, especially those involving blurry boundaries and highly irregular regions of low contrast. In this paper, a SAM-based parameter-efficient fine-tuning method, called SAMIHS, is proposed for intracranial hemorrhage segmentation, which is a crucial and challenging step in stroke diagnosis and surgical planning. Distinguished from previous SAM and SAM-based methods, SAMIHS incorporates parameter-refactoring adapters into SAM's image encoder and considers the efficient and flexible utilization of adapters' parameters. Additionally, we employ a combo loss that combines binary cross-entropy loss and boundary-sensitive loss to enhance SAMIHS's ability to recognize the boundary regions. Our experimental results on two public datasets demonstrate the effectiveness of our proposed method. Code is available at https://github.com/mileswyn/SAMIHS .

翻译：分割一切模型（SAM）作为一种基于大规模标注训练而成的视觉基础模型，近年来在医学图像分割领域持续引发关注。尽管SAM在自然场景中展现出卓越能力，但当面对医学图像（尤其是那些包含模糊边界与低对比度高度不规则区域的图像）时，其性能会显著下降。本文提出了一种基于SAM的参数高效微调方法SAMIHS，用于颅内出血分割——这是脑卒中诊断与手术规划中关键且具挑战性的步骤。与先前SAM及基于SAM的方法不同，SAMIHS在SAM的图像编码器中引入了参数重构适配器，并考虑了适配器参数的高效灵活利用。此外，我们采用融合二元交叉熵损失与边界敏感损失的组合损失函数，以增强SAMIHS对边界区域的识别能力。在两个公开数据集上的实验结果验证了所提方法的有效性。代码开源地址：https://github.com/mileswyn/SAMIHS