Background and Objective: Shape prior models play a vital role for segmentation in medical image analysis. These models are most effective when shape variations can be captured by a parametric distribution, and sufficient training data is available. However, in the absence of these conditions, results are invariably much poorer. In this paper, we propose a novel shape prior model, via dual subspace segment projection learning (DSSPL), to address these challenges.

Methods: DSSPL serves to compose shapes from an ensemble of shape segments where each segment is formed using two subspaces: global shape subspace and segment-specific subspace, each necessary for extracting global shape patterns and local patterns, respectively. This ensures the proposed approach has general shape plausibility in regions of signal drop-out or missing boundary information, and also more localized flexibility. The learned projections are constrained with l_2,1 sparse norm terms to extract the most distinguishable features, while the reconstructive properties of DSSPL reduces information loss and leverages the subspaces to provide contiguous shapes without any post-processing.

Results: Extensive analysis is performed on three databases from different medical imaging systems across X-Ray, MRI, and ultrasound. DSSPL outperforms all compared benchmarks in terms of shape generalization ability and segmentation performance.

Conclusions: We propose a new shape prior model for segmentation in medical image analysis to address the challenges of modelling complex organ shapes with low sample size training data.

背景与目的：形状先验模型对于医学图像分析中的分割起着至关重要的作用。当可以通过参数分布捕获形状变化并且有足够的训练数据时，这些模型最有效。但是，在没有这些条件的情况下，结果总是差得多。在本文中，我们通过双重子空间分段投影学习（DSSPL）提出了一种新颖的形状先验模型，以应对这些挑战。

方法： DSSPL用于从形状段的集合中组合形状，其中每个段是使用两个子空间形成的：全局形状子空间和特定于段的子空间，分别提取全局形状图案和局部图案是必需的。这确保了所提出的方法在信号丢失或缺少边界信息的区域中具有一般的形状合理性，并且具有更大的局部灵活性。学习的投影受l _2,1稀疏范数约束，以提取最可区别的特征，而DSSPL的重建特性减少了信息丢失，并利用子空间提供了连续的形状，而无需任何后处理。

结果：在来自不同医学成像系统的三个数据库中，对X射线，MRI和超声进行了广泛的分析。在形状泛化能力和分割性能方面，DSSPL优于所有比较基准。

结论：我们提出了一种新的形状先验模型，用于医学图像分析中的分割，以解决使用低样本量训练数据来建模复杂器官形状的挑战。