A new pipeline to automatically segment and semi-automatically measure bone length on 3D models obtained by Computed Tomography

Abstract

statement: Beltran Diaz et al. present a semi-automated pipeline for fast and versatile characterization of bone length from micro-CT images of mouse developmental samples. Abstract:The characterization of developmental phenotypes often relies on the accurate linear measurement of structures that are small and require laborious preparation. This is tedious and prone to errors, especially when repeated for the multiple replicates that are required for statistical analysis, or when multiple distinct structures have to be analysed. To address this issue, we have developed a pipeline for characterization of long-bone length using micro-CT scans. It involves a semi-automated algorithm that uses the Mimics Innovation Suite package (Materialise) for automatic thresholding and fast interactive isolation and 3D-model generation of the main limb bones. All the image-processing steps are included in a user-friendly Python script. We show that the appropriate combination of scanning and in silico analysis conditions yields fast and reproducible length results, highly correlated with the measurements obtained via ex vivo skeletal preparations. Moreover, since micro-CT is not destructive, the samples can be used afterwards for histology or other applications. Our new pipeline will help developmental biologists and evolution researchers to achieve fast, reproducible and non-destructive length measurement of bone samples from multiple animal species.

A high-throughput semi-automated bone segmentation workflow for murine hindpaw micro-CT datasets

“…Importantly, this novel segmentation approach dramatically reduces the potential for inter-user bias and inaccuracies inherent with the conventional analysis methods of manual or density-based segmentation. As manual segmentation places the responsibility on the user to accurately detect the edge across the bone volume, this process is both time-consuming and prone to user error ( Iassonov et al, 2009 ; Diaz et al, 2021 ). On the other hand, the density-based approach relies on strict parameters for threshold selection to separate structures that may similarly introduce inaccuracies at the edges ( Rathnayaka et al, 2011 ), and the stringent parameters make the adoption for comparable datasets difficult.…”

“…However, in complex structures such as the murine hindpaw, which contains 30 or 31 (as the fused navicular/lateral cuneiform ( Richbourg et al, 2017 ) can also be variably fused with the intermediate cuneiform in C57BL/6 mice) distinct bones of various sizes and shapes, no strategies have been widely adopted to segment these individual bones for high-throughput and reproducible analysis. The conventional approaches to μCT analysis in structures such as the hindpaw are solely reliant on manual contouring or density-based thresholding approaches ( Proulx et al, 2007 ) that are prone to inaccuracies at bone edges ( Iassonov et al, 2009 ; Diaz et al, 2021 ; Rathnayaka et al, 2011 ). Given the intensive segmentation efforts involved in further analysis, data analysis is typically limited to a single ( Proulx et al, 2007 ), small subset, and/or focal regions of bones ( Cambre et al, 2018 ).…”