SAMI3D-DW：任意三维医学图像的交互式分割
SAMI3D-DW: Interactive Segmentation of Any 3D Medical Images
- Deepwise Healthcare（深睿医疗）
- The University of Hong Kong（香港大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出SAMI3D-DW，一种基于大规模专有数据训练的交互式3D分割模型，在CT/MR基准上以类别平衡DSC取得最优性能，并显著加速NF1肿瘤标注。
AI中文摘要：
三维医学图像的交互式分割支持对解剖结构和疾病的定量分析，同时允许用户指定并细化其目标。尽管nnInteractive和VISTA3D已取得显著进展，但在多样化的临床目标上实现可靠分割仍具挑战性，尤其是对于复杂解剖结构以及异质性、长尾分布的病理谱系。我们提出了SAMI3D-DW V1（以下简称SAMI3D-DW），这是一个基于Deepwise大规模专有医学影像数据集训练的交互式三维分割模型。我们在一个包含来自219个源数据集的4326例CT/MR病例的基准上，通过模拟用户交互对模型进行评估，该基准涵盖107个解剖和病理类别，按医学分类法组织，并使用类别平衡的DSC分数进行评估。在两种交互模式下，SAMI3D-DW均取得了评估方法中最高的类别宏平均Dice分数。使用一个点，其得分为0.5764，而最强基线nnInteractive为0.5315；使用五个点时，得分升至0.7771，对比nnInteractive的0.7494。使用边界框初始化时，得分为0.7130，对比nnInteractive的0.6530。经过五次修正点击后，SAMI3D-DW达到0.8002，对比nnInteractive的0.7868，使其成为唯一超过0.80的评估中支持框的模型。对于放射科医生和临床医生，SAMI3D-DW能够通过几次点击分割复杂解剖结构，包括CT和MR血管造影中的颅内血管树。在一项涉及神经纤维瘤病1型（NF1）的初步内部比较中，SAMI3D-DW辅助的肿瘤标注每个病例仅需数分钟，约为手动标注所需时间的十五分之一，凸显了其支持体积治疗反应评估的潜力。
英文摘要：
Interactive segmentation of 3D medical images supports quantitative analysis of anatomical structures and disease while allowing users to specify and refine their targets. Despite substantial progress by nnInteractive and VISTA3D, reliable segmentation across diverse clinical targets remains challenging, particularly for complex anatomical structures and the heterogeneous, long-tailed spectrum of pathology. We present SAMI3D-DW V1 (hereafter SAMI3D-DW), an interactive 3D segmentation model trained on Deepwise's large-scale proprietary medical image datasets. We evaluate the model under simulated user interactions on a CT/MR benchmark comprising 4,326 cases from 219 source datasets, spanning 107 anatomical and pathological categories, organized by a medical taxonomy and evaluated with a category-balanced DSC score. SAMI3D-DW achieves the highest category-macro Dice among evaluated methods in both interaction modes. With one point, it scores 0.5756 versus 0.5316 for nnInteractive, the strongest baseline, rising to 0.7771 versus 0.7495 with five points. With bounding-box initialization, the scores are 0.7129 versus 0.6530. After five corrective clicks, SAMI3D-DW reaches 0.8004 versus 0.7868. For radiologists and clinicians, SAMI3D-DW enables segmentation of complex anatomical structures, including intracranial vessel trees on CT and MR angiography, with a few clicks. In a preliminary in-house comparison involving neurofibromatosis type 1 (NF1), SAMI3D-DW-assisted tumor annotation took minutes per case and approximately one-fifteenth of the time required for manual annotation, highlighting its potential to support volumetric treatment-response assessment.
