基于框提示MedSAM的点监督脑肿瘤分割
Point-supervised Brain Tumor Segmentation with Box-prompted MedSAM
AI总结：
本文提出一种迭代框架，通过语义框提示生成器和提示引导空间细化模块将点标注转化为伪边界框并利用MedSAM进行脑肿瘤分割，在BraTS2018上性能优于传统点监督方法且与框监督方法相当。
摘要：
勾画病灶和解剖结构对于图像引导干预非常重要。点监督医学图像分割（PSS）在减轻昂贵的专家勾画标注方面具有巨大潜力。然而，由于缺乏精确的尺寸和边界引导，PSS的有效性往往达不到预期。尽管最近的视觉基础模型，如医学分割任意模型（MedSAM），在边界框提示分割方面取得了显著进展，但直接利用点标注并不简单，并且容易出现语义歧义。在这项初步研究中，我们引入了一个迭代框架，以促进语义感知的点监督MedSAM。具体而言，语义框提示生成器（SBPG）模块能够将点输入转换为潜在的伪边界框建议，并通过基于原型的语义相似性对其进行显式细化。随后是提示引导空间细化（PGSR）模块，该模块利用MedSAM卓越的泛化能力来推断分割掩码，同时更新SBPG中的框提议种子。通过充分的迭代，性能可以逐步提升。我们在BraTS2018上对全脑肿瘤分割进行了评估，并证明其性能优于传统的PSS方法，且与框监督方法相当。
英文摘要：
Delineating lesions and anatomical structure is important for image-guided interventions. Point-supervised medical image segmentation (PSS) has great potential to alleviate costly expert delineation labeling. However, due to the lack of precise size and boundary guidance, the effectiveness of PSS often falls short of expectations. Although recent vision foundational models, such as the medical segment anything model (MedSAM), have made significant advancements in bounding-box-prompted segmentation, it is not straightforward to utilize point annotation, and is prone to semantic ambiguity. In this preliminary study, we introduce an iterative framework to facilitate semantic-aware point-supervised MedSAM. Specifically, the semantic box-prompt generator (SBPG) module has the capacity to convert the point input into potential pseudo bounding box suggestions, which are explicitly refined by the prototype-based semantic similarity. This is then succeeded by a prompt-guided spatial refinement (PGSR) module that harnesses the exceptional generalizability of MedSAM to infer the segmentation mask, which also updates the box proposal seed in SBPG. Performance can be progressively improved with adequate iterations. We conducted an evaluation on BraTS2018 for the segmentation of whole brain tumors and demonstrated its superior performance compared to traditional PSS methods and on par with box-supervised methods.
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