用时空统一建模提升肺结节随访CT分割精度
OmniMamba4D: Spatio-temporal Mamba for longitudinal CT lesion segmentation
- 设计四维定向Mamba块，同时捕捉空间与时间特征
- 在3252张扫描数据上达0.682的Dice分数，优于传统方法
- 适合需追踪病灶变化的临床随访场景
准确分割纵向CT扫描对监测肿瘤进展和评估治疗反应至关重要。然而，现有3D分割模型仅关注空间信息。为弥补这一不足，我们提出OmniMamba4D，一种专为4D医学图像（三维图像随时间变化）设计的分割模型。该模型采用时空四向量Mamba块，有效捕获空间与时间特征。不同于仅分析单一时相的3D模型，OmniMamba4D能处理4D CT数据，提供病灶演变的完整时空信息。在包含3,252张CT扫描的内部数据集上，其达到0.682的竞品级Dice分数，同时保持计算高效，并更优检测消失病灶。本工作展示了一种利用时空信息进行纵向CT病灶分割的新框架。
原文摘要 · Abstract (English)
Accurate segmentation of longitudinal CT scans is important for monitoring tumor progression and evaluating treatment responses. However, existing 3D segmentation models solely focus on spatial information. To address this gap, we propose OmniMamba4D, a novel segmentation model designed for 4D medical images (3D images over time). OmniMamba4D utilizes a spatio-temporal tetra-orientated Mamba block to effectively capture both spatial and temporal features. Unlike traditional 3D models, which analyze single-time points, OmniMamba4D processes 4D CT data, providing comprehensive spatio-temporal information on lesion progression. Evaluated on an internal dataset comprising of 3,252 CT scans, OmniMamba4D achieves a competitive Dice score of 0.682, comparable to state-of-the-arts (SOTA) models, while maintaining computational efficiency and better detecting disappeared lesions. This work demonstrates a new framework to leverage spatio-temporal information for longitudinal CT lesion segmentation.
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