Introduction

Introduction

CurveAlign is a Curvelet transform (CT)-based quantitative tool originally designed for interpreting the regional interaction between fibrillar collagen and tumors, later on evolved to generate up to ~thirty fiber features including orientation, density, alignment, and etc. from four different fiber analysis modes. CT-FIRE analyzes individual fiber metrics such as length, width, angle, and curvature by combining the advantages of the fast discrete curvelet transform (Candes, et al 2006) for denoising images, enhancement of the fiber edge features, and the fiber extraction (FIRE) algorithm (Stein, et al 2008) for extracting individual fibers.

These two tools were developed with complementary but different main goals. CurveAlign(Bredfeldt et al., 2014a; Pavone and Campagnola, 2013; Liu et al., 2017; Liu et al, 2020) was developed first and had the main goal of quantifying all fiber angles within a region of interest relative to a user defined boundary be it a line or a tumor boundary. As our research grew in investigating the role of collagen in cancer progression and invasion, we wanted to investigate how individual fiber parameters could influence cancer and other diseases. Out of this need came the development of CT-FIRE(Bredfeldt et al.; 2014b, Liu et al., 2020) to analyze individual fiber metrics such as length, width, angle, and curvature. Besides the relative angle quantification with respect to the boundary, the current version of CurveAlign can be used to extract other collagen fiber features, such as localized fiber density, fiber alignment, and the spatial relationship between fiber and the associated boundary. In addition, the extracted individual fibers extracted by CT-FIRE can be imported into the CurveAlign for additional feature extraction mentioned above.

Based on these two tools mentioned above, we have been developing a comprehensive fibrillar collagen platform. Recently added modules include C++-based code optimization for fast individual fiber tracking, Java-based synthetic fiber generator module for method validation, automatic tumor boundary generation for fiber relative quantification, parallel computing for large-scale batch mode processing, region-of-interest analysis for user-specified quantification, and pre- and post-processing modules for individual fiber visualization. We have future plans to integrate all these modules together and add machine-learning based feature analysis and classification, real-time fiber analysis as well as make our platform cloud-ready. For now, CurveAlign should be used for bulk assessment of fibrillar collagen features including alignment/density and CT-FIRE for individual fiber quantification.