Collagen fibers in biological tissues have a complex 3D organization containing rich information linked to tissue mechanical properties and are affected by mutations that lead to diseases. Quantitative assessment of this 3D collagen fiber organization could help to develop reliable biomechanical models and understand tissue structure-function relationships, which impact diagnosis and treatment of diseases or injuries. While there are advanced techniques for imaging collagen fibers, published methods for quantifying 3D collagen fiber organization have been sparse and give limited structural information which cannot distinguish a wide range of 3D organizations. In this article, we demonstrate an algorithm for quantitative classification of 3D collagen fiber organization. The algorithm first simulates five groups, or classifications, of fiber organization: unidirectional, crimped, disordered, two-fiber family, and helical. These five groups are widespread in natural tissues and are known to affect the tissue’s mechanical properties. We use quantitative metrics based on features such as preferred 3D fiber orientation and spherical variance to differentiate each classification in a repeatable manner. We validate our algorithm by applying it to second-harmonic generation images of collagen fibers in tendon and cervix tissue that has been sectioned in specified orientations, and we find strong agreement between classification from simulated data and the physical fiber organization. Our approach provides insight for interpreting 3D fiber organization directly from volumetric images. This algorithm could be applied to other fiber-like structures that are not necessarily made of collagen.

中文翻译：

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根据体积图像对 3D 胶原纤维组织进行定量分类


生物组织中的胶原纤维具有复杂的 3D 组织，包含与组织机械特性相关的丰富信息，并受到导致疾病的突变的影响。对这种 3D 胶原纤维组织的定量评估可以帮助开发可靠的生物力学模型并了解组织结构-功能关系，从而影响疾病或损伤的诊断和治疗。尽管有先进的胶原纤维成像技术，但已发表的用于量化 3D 胶原纤维组织的方法很少，并且提供的结构信息有限，无法区分广泛的 3D 组织。在本文中，我们演示了一种对 3D 胶原纤维组织进行定量分类的算法。该算法首先模拟纤维组织的五组或分类：单向、卷曲、无序、双纤维族和螺旋。这五组广泛存在于天然组织中，并且已知会影响组织的机械性能。我们使用基于首选 3D 纤维方向和球形方差等特征的定量指标，以可重复的方式区分每个分类。我们通过将算法应用于已按指定方向切片的肌腱和宫颈组织中胶原纤维的二次谐波生成图像来验证我们的算法，并且我们发现模拟数据的分类与物理纤维组织之间存在很强的一致性。我们的方法提供了直接从体积图像解释 3D 纤维组织的见解。该算法可以应用于不一定由胶原蛋白制成的其他纤维状结构。