[论文解读] Constrained Multi-shape Evolution for Overlapping Cytoplasm Segmentation

Youyi Song, Lei Zhu+5 more|arXiv (Cornell University)|Apr 8, 2020

AI in cancer detection参考文献 39被引用 4

一句话总结

本文提出了一种约束性多形态演化方法，通过联合演化多个细胞质形态，利用局部（细胞质级）和全局（团块级）形状先验，实现宫颈涂片图像中重叠细胞质的分割。该方法从细胞质实例的统计形状数据中构建无限大的形状假设集合，于演化过程中施加形状约束，并通过补偿重叠区域的强度不足，实现了优于最先进方法的分割精度。

ABSTRACT

Segmenting overlapping cytoplasm of cells in cervical smear images is a clinically essential task, for quantitatively measuring cell-level features in order to diagnose cervical cancer. This task, however, remains rather challenging, mainly due to the deficiency of intensity (or color) information in the overlapping region. Although shape prior-based models that compensate intensity deficiency by introducing prior shape information (shape priors) about cytoplasm are firmly established, they often yield visually implausible results, mainly because they model shape priors only by limited shape hypotheses about cytoplasm, exploit cytoplasm-level shape priors alone, and impose no shape constraint on the resulting shape of the cytoplasm. In this paper, we present a novel and effective shape prior-based approach, called constrained multi-shape evolution, that segments all overlapping cytoplasms in the clump simultaneously by jointly evolving each cytoplasm's shape guided by the modeled shape priors. We model local shape priors (cytoplasm--level) by an infinitely large shape hypothesis set which contains all possible shapes of the cytoplasm. In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors (clump--level) modeled by considering mutual shape constraints of cytoplasms in the clump. We also constrain the resulting shape in each evolution to be in the built shape hypothesis set, for further reducing implausible segmentation results. We evaluated the proposed method in two typical cervical smear datasets, and the extensive experimental results show that the proposed method is effective to segment overlapping cytoplasm, consistently outperforming the state-of-the-art methods.

研究动机与目标

• 为解决宫颈涂片图像中重叠细胞质分割的挑战，其中由于重叠区域的存在，强度信息往往不足。
• 克服现有基于形状先验的方法的局限性，这些方法仅使用有限的形状假设，且对生成形状缺乏约束。
• 通过联合演化多个细胞质形态，结合局部与全局形状先验，提升分割精度。
• 通过将演化形状约束在学习得到的全面形状假设集合内，确保分割结果具有物理上的合理性。

提出的方法

• 该方法利用从细胞质实例统计形状分析中导出的无限大形状假设集合，建模局部形状先验。
• 提出一种学习算法以计算形状样本的重要性，使该假设集合能够表示所有可能的细胞质形状。
• 通过建模细胞团内各细胞质之间的相互形状约束，引入全局形状先验，提升重叠区域的一致性。
• 在演化过程中，同时利用局部与全局形状先验引导形状演化，以补偿强度不足。
• 在每个演化步骤中，将生成的形状约束在学习得到的形状假设集合内，以防止生成不合理的形状。
• 提供了收敛性分析，证明重要性学习算法无论输入顺序如何，均能收敛至最优权重。

实验结果

研究问题

• RQ1 是否一个包含所有可能细胞质形状的全面形状假设集合，能够提升重叠区域的分割精度？
• RQ2 在团块内细胞质之间引入相互约束的全局形状先验，对分割质量有何影响？
• RQ3 将演化形状约束在学习得到的形状假设集合内，是否能有效减少不合理的视觉分割结果？
• RQ4 所提出的方法在重叠细胞质分割任务中，是否在准确性和视觉合理性方面均优于现有基于形状先验的方法？
• RQ5 形状统计的重要性学习算法在不同输入顺序下是否具有鲁棒性且能收敛？

主要发现

• 所提出方法在两个典型宫颈涂片图像数据集上，始终优于最先进方法，分割精度表现更优。
• 同时使用局部与全局形状先验，显著提升了在强度不足的重叠区域的分割性能。
• 将演化形状约束在学习得到的形状假设集合内，能有效减少视觉上不合理的分割结果。
• 重要性学习算法被证明可收敛至最优权重，且收敛性与输入顺序无关。
• 通过联合演化多个细胞质形态，该方法实现了卓越性能，同时保持了团块内结构的一致性。