Journal article
MSO-GP: 3-D segmentation of large and complex conjoined tree structures
Methods (San Diego, Calif.), Vol.229, pp.9-16
09/2024
DOI: 10.1016/j.ymeth.2024.05.016
PMID: 38838947
Abstract
Robust segmentation of large and complex conjoined tree structures in 3-D is a major challenge in computer vision. This is particularly true in computational biology, where we often encounter large data structures in size, but few in number, which poses a hard problem for learning algorithms. We show that merging multiscale opening with geodesic path propagation, can shed new light on this classic machine vision challenge, while circumventing the learning issue by developing an unsupervised visual geometry approach (digital topology/morphometry). The novelty of the proposed MSO-GP method comes from the geodesic path propagation being guided by a skeletonization of the conjoined structure that helps to achieve robust segmentation results in a particularly challenging task in this area, that of artery-vein separation from non-contrast pulmonary computed tomography angiograms. This is an important first step in measuring vascular geometry to then diagnose pulmonary diseases and to develop image-based phenotypes. We first present proof-of-concept results on synthetic data, and then verify the performance on pig lung and human lung data with less segmentation time and user intervention needs than those of the competing methods.
•We revisit the classic computer vision problem of segmenting complex conjoined tree-like structures.•We the branches as skeleton guided geodesic paths and open them morphologically in multiple scales.•The proposed MSO-GP method is unsupervised and non-learning based, hence fast and annotation independent.•We demonstrate the method on artery-vein segmentation in non contrast lung computed tomography (CT) angiograms.•MSO-GP outperforms two competing methods on synthetic generated data, pig lung phantom data and human lung data.
Details
- Title: Subtitle
- MSO-GP: 3-D segmentation of large and complex conjoined tree structures
- Creators
- Arijit De - Jadavpur UniversityNirmal Das - Jadavpur UniversityPunam K. Saha - University of IowaAlejandro Comellas - University of IowaEric Hoffman - University of IowaSubhadip Basu - University of Engineering & ManagementTapabrata Chakraborti - The Alan Turing Institute
- Resource Type
- Journal article
- Publication Details
- Methods (San Diego, Calif.), Vol.229, pp.9-16
- DOI
- 10.1016/j.ymeth.2024.05.016
- PMID
- 38838947
- NLM abbreviation
- Methods
- ISSN
- 1046-2023
- eISSN
- 1095-9130
- Publisher
- Elsevier Inc
- Grant note
- Turing-Roche Strategic Partnership
- Language
- English
- Date published
- 09/2024
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Pulmonary, Critical Care, and Occupational Medicine; ICTS; Internal Medicine
- Record Identifier
- 9984641860102771
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