Spectral Domain Optical Coherence Tomography (SD-OCT) is an effective tool for volumetric imaging of collagen fiber networks, but current processing algorithms struggle to create three-dimensional models of these networks due to limited contrast and tissue complexity. We present an automated image processing algorithm that overcomes these challenges to enable quantitative visualization of three-dimensional fiber networks from OCT volumes. Samples are processed by segmenting the tissue volume surface and dividing the sample into “processing patches” which are optimally sized and oriented to fit an arbitrary volume. Fiber orientation analysis and particle filtering are used to create orientation-encoded fiber tractography. The method is demonstrated on five ex-vivo human uterine samples which were imaged as mosaic volumes using a commercial SD-OCT system, providing the first view of the three-dimensional structure of the human uterine collagen fiber network on a centimeter scale.
An image processing method was developed for automatically generating three-dimensional models of human uterine collagen fiber networks from spectral-domain optical coherence tomography (SD-OCT) images. A novel image processing scheme was used to segment the tissue from the image volume and divide it into “processing regions” that adapt to the tissue topology. Quantitative fiber analysis was performed on b-scan and en-face image patches, enabled by a custom b-scan preprocessing pipeline. Three-dimensional fiber architecture was analyzed in over 100 mosaic volumes from five different patients (2 pregnant, 3 non-pregnant), revealing a highly complex and patient-specific network of fibrous collagen and myocyte bundles.
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