评估医学版分割模型在3D心脏MRI中自动分割左心房的性能。
Assessing Foundational Medical 'Segment Anything' (Med-SAM1, Med-SAM2) Deep Learning Models for Left Atrial Segmentation in 3D LGE MRI
- 用医学专用分割模型自动化左心房分割
- MedSAM2比MedSAM1在相同提示下表现更优
- 提示框大小和位置影响分割精度，需谨慎设计
心房颤动（AF）是最常见的心律失常，与心力衰竭和中风相关。在3D延迟钆增强磁共振成像（LGE-MRI）中准确分割左心房（LA）有助于评估AF，因为心肌纤维化重塑是心律失常的关键因素，并决定治疗策略。然而，手动分割左心房耗时费力且具有挑战性。近期，基于通用数据集预训练的分割基础模型（如分割一切模型SAM）在通用分割任务中表现出色。MedSAM是专为医学应用微调的SAM版本，可在无需特定领域训练的情况下实现高效的零样本分割。尽管MedSAM潜力巨大，其在3D LGE-MRI中复杂左心房分割任务中的表现尚未评估。本研究旨在：(1) 评估MedSAM在自动化左心房分割中的性能；(2) 比较使用单个提示并自动追踪的MedSAM2模型与每个切片需单独提示的MedSAM1模型的性能；(3) 分析不同提示框大小和位置对MedSAM1模型Dice分数（即分割准确率）的影响。
原文摘要 · Abstract (English)
Atrial fibrillation (AF), the most common cardiac arrhythmia, is associated with heart failure and stroke. Accurate segmentation of the left atrium (LA) in 3D late gadolinium-enhanced (LGE) MRI is helpful for evaluating AF, as fibrotic remodeling in the LA myocardium contributes to arrhythmia and serves as a key determinant of therapeutic strategies. However, manual LA segmentation is labor-intensive and challenging. Recent foundational deep learning models, such as the Segment Anything Model (SAM), pre-trained on diverse datasets, have demonstrated promise in generic segmentation tasks. MedSAM, a fine-tuned version of SAM for medical applications, enables efficient, zero-shot segmentation without domain-specific training. Despite the potential of MedSAM model, it has not yet been evaluated for the complex task of LA segmentation in 3D LGE-MRI. This study aims to (1) evaluate the performance of MedSAM in automating LA segmentation, (2) compare the performance of the MedSAM2 model, which uses a single prompt with automated tracking, with the MedSAM1 model, which requires separate prompt for each slice, and (3) analyze the performance of MedSAM1 in terms of Dice score(i.e., segmentation accuracy) by varying the size and location of the box prompt.
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