3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. Today fully-convolutional networks (FCNs), while dominant, do not offer these capabilities. We present deep implicit statistical shape models (DISSMs), a new approach to delineation that marries the representation power of convolutional neural networks (CNNs) with the robustness of SSMs. DISSMs use a deep implicit surface representation to produce a compact and descriptive shape latent space that permits statistical models of anatomical variance. To reliably fit anatomically plausible shapes to an image, we introduce a novel rigid and non-rigid pose estimation pipeline that is modelled as a Markov decision process(MDP). We outline a training regime that includes inverted episodic training and a deep realization of marginal space learning (MSL). Intra-dataset experiments on the task of pathological liver segmentation demonstrate that DISSMs can perform more robustly than three leading FCN models, including nnU-Net: reducing the mean Hausdorff distance (HD) by 7.7-14.3mm and improving the worst case Dice-Sorensen coefficient (DSC) by 1.2-2.3%. More critically, cross-dataset experiments on a dataset directly reflecting clinical deployment scenarios demonstrate that DISSMs improve the mean DSC and HD by 3.5-5.9% and 12.3-24.5mm, respectively, and the worst-case DSC by 5.4-7.3%. These improvements are over and above any benefits from representing delineations with high-quality surface.
翻译:3D 解剖结构的划界是医学成像分析的一项首要目标。在深层学习之前,具有解剖限制和产生高质量表面的统计形状模型是一种核心技术。在深层学习之前,具有解剖限制和产生高质量表面的统计形状模型是一种核心技术。今天,完全革命性网络(FCNs)虽然占主导地位,但却不能提供这些能力。我们展示了深度隐含统计形状模型(DISMs),这是一种将共振神经网络(CNNs)的代表性能力与SSSMs强力相结合的新的划界方法。DISMs使用深层隐含的表层代表制模型来产生一个允许解剖解剖限制和产生高质量表面的统计形状模型。DISMSMs 5 - 高级神经神经网络(DISC) 的深度分析显示,DRCs-9-14SDMSM 的深度分析,通过直流化数据分析,使DISSM(S-R) 的稳定性和深度数据质量(MSL) 改进。