The Segment Anything Model (SAM) has recently emerged as a groundbreaking model in the field of image segmentation. Nevertheless, both the original SAM and its medical adaptations necessitate slice-by-slice annotations, which directly increase the annotation workload with the size of the dataset. We propose MedLSAM to address this issue, ensuring a constant annotation workload irrespective of dataset size and thereby simplifying the annotation process. Our model introduces a few-shot localization framework capable of localizing any target anatomical part within the body. To achieve this, we develop a Localize Anything Model for 3D Medical Images (MedLAM), utilizing two self-supervision tasks: relative distance regression (RDR) and multi-scale similarity (MSS) across a comprehensive dataset of 14,012 CT scans. We then establish a methodology for accurate segmentation by integrating MedLAM with SAM. By annotating only six extreme points across three directions on a few templates, our model can autonomously identify the target anatomical region on all data scheduled for annotation. This allows our framework to generate a 2D bounding box for every slice of the image, which are then leveraged by SAM to carry out segmentations. We conducted experiments on two 3D datasets covering 38 organs and found that MedLSAM matches the performance of SAM and its medical adaptations while requiring only minimal extreme point annotations for the entire dataset. Furthermore, MedLAM has the potential to be seamlessly integrated with future 3D SAM models, paving the way for enhanced performance. Our code is public at \href{https://github.com/openmedlab/MedLSAM}{https://github.com/openmedlab/MedLSAM}.

翻译：分段任意模型（SAM）近期已成为图像分割领域的开创性模型。然而，原始SAM及其医学适配版本均需逐切片标注，这将直接导致标注工作量随数据集规模增加而上升。我们提出MedLSAM以解决该问题，确保标注工作量与数据集规模无关，从而简化标注流程。本模型引入少样本定位框架，可定位体内任意目标解剖部位。为此，我们开发了面向3D医学图像的任意定位模型（MedLAM），基于包含14,012次CT扫描的综合数据集，采用两项自监督任务：相对距离回归（RDR）与多尺度相似性（MSS）。随后通过将MedLAM与SAM集成，建立精准分割方法体系。仅需在少数模板的3个方向上标注6个极值点，本模型即可在所有待标注数据上自主识别目标解剖区域。该框架可为图像每个切片生成2D边界框，进而由SAM执行分割。我们在覆盖38个器官的两个3D数据集上开展实验，结果表明MedLSAM在仅需整个数据集极少量极值点标注的情况下，实现了与SAM及其医学适配版本相当的性能。此外，MedLAM具有与未来3D SAM模型无缝集成的潜力，为性能提升奠定基础。我们的代码已公开于\href{https://github.com/openmedlab/MedLSAM}{https://github.com/openmedlab/MedLSAM}。