Scalable Robust Graph and Feature Extraction for Arbitrary Vessel Networks in Large Volumetric Datasets

Drees et al. BMC Bioinformatics (2021) 22:346 https://doi.org/10.1186/s12859-021-04262-w METHODOLOGY ARTICLE Open Access Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets Dominik Drees1, Aaron Scherzinger2, René Hägerling4, Friedemann Kiefer3 and Xiaoyi Jiang1* *Correspondence: [email protected] Abstract 1 Faculty of Mathematics Background: Recent advances in 3D imaging technologies provide novel insights to and Computer Science, University of Münster, researchers and reveal fner and more detail of examined specimen, especially in the Münster, Germany biomedical domain, but also impose huge challenges regarding scalability for auto- Full list of author information mated analysis algorithms due to rapidly increasing dataset sizes. In particular, existing is available at the end of the article research towards automated vessel network analysis does not always consider memory requirements of proposed algorithms and often generates a large number of spuri- ous branches for structures consisting of many voxels. Additionally, very often these algorithms have further restrictions such as the limitation to tree topologies or relying on the properties of specifc image modalities. Results: We propose a scalable iterative pipeline (in terms of computational cost, required main memory and robustness) that extracts an annotated abstract graph rep- resentation from the foreground segmentation of vessel networks of arbitrary topology and vessel shape. The novel iterative refnement process is controlled by a single, dimensionless, a-priori determinable parameter. Conclusions: We are able to, for the frst time, analyze the topology of volumes of roughly 1 TB on commodity hardware, using the proposed pipeline. We demonstrate improved robustness in terms of surface noise, vessel shape deviation and anisotropic resolution compared to the state of the art. An implementation of the presented pipeline is publicly available in version 5.1 of the volume rendering and processing engine Voreen. Keywords: Graph extraction, Vessel network, Scalability, Large volume processing Background As one of the important problems in biomedical imaging, segmentation provides the foundation for tasks like quantifcation and diagnostics [1]. In particular, the study of vascular networks using modern 3D imaging techniques has become an increasingly popular topic of interest in biomedical research [2–7], as 2D slice analysis is limited to a small subset of the available data and cannot capture the 3D structure of vessel networks [8]. At the same time, analysis of 3D datasets by visual inspection is error prone © The Author(s), 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Drees et al. BMC Bioinformatics (2021) 22:346 Page 2 of 28 [9]. Tus, for quantifable results or novel insights into data properties invisible to the human observer, automatic image processing techniques are required. In light of diferent imaging techniques, modalities and problem domains, the generation of a voxel-wise foreground segmentation can be seen as a sensible intermediate step for the automatic processing of vascular network images (see Fig. 1). However, the next step of calculating topological, morphological or geometric features—a key requirement for the application in biomedical research and beyond—has not received sufcient attention from the research community [10], which mostly focused on developing novel methods [11–13] and software [14, 15] in the segmentation domain. In recent years, improvements in imaging technology and procedures are providing images of increasing resolution (in terms of the number of voxels per unit volume) and thus ofer new chances for fner grained analysis, but also pose new challenges for image analysis algorithms. Te analysis of fne structures down to the level of capillary networks promises great insights. At the same time it is desirable to analyze large networks at once in order to obtain as much topological information as possible and avoid arti- facts at the boundary of the volume. Modern microscopy hardware generates a single volumetric image fle of hundreds of GBs or even TBs (the same order of magnitude as hard drive capacity) per sample which existing vessel analysis approaches are not able to process. Te problem of rapid advances in technology and increasing data sizes is also present in other domains such as cell type identifcation [16], but is especially pressing in 3D image analysis where datasets exceed the main memory of not only commodity hardware but also specialized workstations. Consequently, for a vessel network analysis pipeline to be useful now and in the future, it has to be scalable both in terms of memory and runtime, and it should be invariant with regard to increases in image resolution. For application in a wide range of biomedical domains, a pipeline should further not rely on specifc imaging conditions, dataset-dependent parameters, a cylindrical vessel shape, isotropic image resolution or a specifc network topology. In this paper we present a pipeline designed to fulfll these requirements while extract- ing the topology, centerlines and edge associated features from binary volumetric images. Tis is achieved by our main contribution, a novel iterative refnement approach and careful design of all pipeline stages. Generally, analyzing ultra large datasets by scalable algorithms is still a huge chal- lenge. For instance, even in the very recent image segmentation tool Biomedisa [17] (based on the popular random walker) a dataset of 900 × 1303 × 4327 voxels had to Fig. 1 Typical pipeline for processing of 3D images of vascular networks. While the research community has mostly focused on the foreground segmentation task (e.g., [47]), this paper presents a widely applicable analysis based on (potentially very large) binary volumes as input Drees et al. BMC Bioinformatics (2021) 22:346 Page 3 of 28 be split for processing, despite testing on a system with amply resources (750 GB RAM (random access memory), 4 NVIDIA Tesla V100) compared to the standard PC (per- sonal computer) as intended in our work. In [18] we presented a hierarchical segmentation algorithm. High-quality segmentation could be demonstrated on datasets of 9070 × 12732 × 1634 voxels (377 GB, 37 times the size of the dataset processed by Bio- medisa) on a standard PC. Tis paper represents another work on scalable algorithm development towards robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets. Related work Chen et al. [19] use the neighborhood-based voxel classifcation defnitions of [20] for analysis of hepatic vasculature. Tey also discuss other alternative voxel-based skeleton extraction methods, but note that they are not suitable: Distance transformation-based methods [21] do not guarantee connectedness of the extracted skeleton, while Voronoi- skeleton methods [22] are comparatively time consuming. Furthermore, Chen et al. [19] perform extensive analysis to denote a single voxel of the skeleton as a branch point. Tey extract a graph by following skeleton voxels in a tree search, breaking up cycles in the graph. Tey do not perform any operations to remove erroneous branches—likely because for the processed low resolution volumes the efect of noise is negligible. Drechsler and Laura [23] extend [19] by decomposing the generated skeleton into segments, computing a number of properties for each segment and generating a labeled graph structure from the segments. Te authors observe that their algorithm is very sen- sitive to noise in the foreground segmentation, but do not propose any means to reduce this efect. Palágyi [24, 25] proposes isthmus-based [26] thinning algorithms and shows that they reduce the sensitivity compared to other approaches, although they do not completely eliminate the problem. Chen et al. [27] present an alternative thinning-based skeletonization method for analysis of hepatic vasculature as part of the previously published pipeline [19]. Tey use the voxel classifcation methods of [20], but present a diferent algorithm. As a preprocess- ing step they suggest flling-hole techniques and morphological operations to remove cavities and irregularities on the surface of the object. Chothani et al. [28] propose a pipeline for tracing of neurites from light microscopy image stacks. It comprises foreground segmentation, skeletonization using a voxel cod- ing algorithm [29], tree construction using the scalar feld

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