Abstract. This paper describes a framework for evaluating airway extraction algorithms in a standardized manner and establishing reference segmentations that can be used for future algorithm development. Because of the sheer difficulty of constructing a complete reference standard manually, we propose to construct a reference using results from the algorithms being compared, by splitting each airway tree segmentation result into individual branch segments that are subsequently visually inspected by trained observers. Using the so constructed reference, a total of seven performance measures covering different aspects of segmentation quality are computed. We evaluated 15 airway tree extraction algorithms from different research groups on a diverse set of 20 chest CT scans from subjects ranging from healthy volunteers to patients with severe lung disease, who were scanned at different sites, with several different CT scanner models, and using a variety of scanning protocols and reconstruction parameters.
Purpose To develop a postprocessing algorithm for multiecho chemical‐shift encoded water–fat separation that estimates proton density fat fraction (PDFF) maps over the full dynamic range (0‐100%) using multipeak fat modeling and multipoint search optimization. To assess its accuracy, reproducibility, and agreement with state‐of‐the‐art complex‐based methods, and to evaluate its robustness to artefacts in abdominal PDFF maps. Methods We introduce MAGO (MAGnitude‐Only), a magnitude‐based reconstruction that embodies multipeak liver fat spectral modeling and multipoint optimization, and which is compatible with asymmetric echo acquisitions. MAGO is assessed first for accuracy and reproducibility on publicly available phantom data. Then, MAGO is applied to N = 178 UK Biobank cases, in which its liver PDFF measures are compared using Bland‐Altman analysis with those from a version of the hybrid iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL) algorithm, LiverMultiScan IDEAL (LMS IDEAL, Perspectum Diagnostics Ltd, Oxford, UK). Finally, MAGO is tested on a succession of high field challenging cases for which LMS IDEAL generated artefacts in the PDFF maps. Results Phantom data showed accurate, reproducible MAGO PDFF values across manufacturers, field strengths, and acquisition protocols. Moreover, we report excellent agreement between MAGO and LMS IDEAL for 6‐echo, 1.5 tesla human acquisitions (bias = −0.02% PDFF, 95% confidence interval = ±0.13% PDFF). When tested on 12‐echo, 3 tesla cases from different manufacturers, MAGO was shown to be more robust to artefacts compared to LMS IDEAL. Conclusion MAGO resolves the water–fat ambiguity over the entire fat fraction dynamic range without compromising accuracy, therefore enabling robust PDFF estimation where phase data is inaccessible or unreliable and complex‐based and hybrid methods fail.
Doses for a range of examinations and views using digital X-ray equipment with full-body linear slit scanning capabilities (Statscan) have been compared with those from other published studies. Entrance doses (free-in-air) were measured using a dosimeter, and effective doses were generated using a Monte Carlo simulator. Doses delivered by the linear slit scanning system were significantly lower than those from conventional X-ray equipment. Effective doses were between 9 and 75% of the United Nations Scientific Committee Report on the Effects of Ionising Radiation doses for standard examinations. This dose reduction can be explained by the properties of linear slit scanning technology, including low scatter, beam geometry, the use of a digital detector and the use of higher than usual tube voltages.
Abstract. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a powerful protocol for assessing tumour progression from changes in tissue contrast enhancement. Manual colorectal tumour delineation is a challenging and time consuming task due to the complex enhancement patterns in the 4D sequence. There is a need for a consistent approach to colorectal tumour segmentation in DCE-MRI and we propose a novel method based on detection of the tumour from signal enhancement characteristics of homogeneous tumour subregions and their neighbourhoods. Our method successfully detected 20 of 23 cases with a mean Dice score of 0.68 ± 0.15 compared to expert annotations, which is not significantly different from expert inter-rater variability of 0.73 ± 0.13 and 0.77 ± 0.10. In comparison, a standard DCE-MRI tumour segmentation technique, fuzzy c-means, obtained a Dice score of 0.28 ± 0.17.
Vasculature is known to be of key biological significance, especially in the study of tumors. As such, considerable effort has been focused on the automated segmentation of vasculature in medical and pre-clinical images. The majority of vascular segmentation methods focus on bloodpool labeling methods, however, particularly in the study of tumors it is of particular interest to be able to visualize both perfused and non-perfused vasculature. Imaging vasculature by highlighting the endothelium provides a way to separate the morphology of vasculature from the potentially confounding factor of perfusion. Here we present a method for the segmentation of tumor vasculature in 3D fluorescence microscopy images using signals from the endothelial and surrounding cells. We show that our method can provide complete and semantically meaningful segmentations of complex vasculature using a supervoxel-Markov Random Field approach. We show that in terms of extracting meaningful segmentations of the vasculature, our method out-performs both a state-ofthe- art method, specific to these data, as well as more classical vasculature segmentation methods.
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