Bone fragment segmentation from 3D CT imagery. (June 2018)

• Record Type: Journal Article
• Authors: Shadid, Waseem G.; Willis, Andrew
• Is Part Of: Computerized medical imaging and graphics. Volume 66(2018)
• Page Start: 14
• Page End: 27
• Publication Date: 2018-06
• Subjects: Segmentation -- 3D -- Computed Tomography (CT) -- Bone -- Fragment -- Watershed transform; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2018.02.001
• Languages: English
• ISSNs: 0895-6111

Abstract

Graphical abstract: Highlights Novel segmentation algorithm to extract bone fragment surfaces from 3D CT images. Formulating the Probabilistic Watershed Transform (PWT). Unique probability functions for segmenting bone fragments from 3D CT images. Unique probability function to avoid merging bone fragments in close proximity. Abstract: This paper presents a novel method to segment bone fragments imaged using 3D Computed Tomography (CT). Existing image segmentation solutions often lack accuracy when segmenting internal trabecular and cancellous bone tissues from adjacent soft tissues having similar appearance and often merge regions associated with distinct fragments. These issues create problems in downstream visualization and pre-operative planning applications and impede the development of advanced image-based analysis methods such as virtual fracture reconstruction. The proposed segmentation algorithm uses a probability-based variation of the watershed transform, referred to as the Probabilistic Watershed Transform (PWT). The PWT uses a set of probability distributions, one for each bone fragment, that model the likelihood that a given pixel is a measurement from one of the bone fragments. The likelihood distributions proposed improve upon known shortcomings in competing segmentation methods for bone fragments within CT images. A quantitative evaluation of the bone segmentation results is provided that compare our segmentation results with several leading competing methods as well as human-generated segmentations.