TY - JOUR
T1 - Steerable3D
T2 - An ImageJ plugin for neurovascular enhancement in 3-D segmentation
AU - Miocchi, Paolo
AU - Sierra, Alejandra
AU - Maugeri, Laura
AU - Stefanutti, Eleonora
AU - Abdollahzadeh, Ali
AU - Mangini, Fabio
AU - Moraschi, Marta
AU - Bukreeva, Inna
AU - Massimi, Lorenzo
AU - Brun, Francesco
AU - Tohka, Jussi
AU - Gröhn, Olli
AU - Mittone, Alberto
AU - Bravin, Alberto
AU - Nicaise, Charles
AU - Giove, Federico
AU - Cedola, Alessia
AU - Fratini, Michela
N1 - Funding Information:
Funding: This work was funded by the Italian Ministry of Health [Young Researcher Grant 2013, GR-2013-02358177] and by the European Union‘s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 691110 (MICROBRADAM). In addition part of the project was financially supported by The FISR Project “Tecnopolo di nanotecnologia e fotonica per la medicina di precisione” (funded by MIUR/CNR, CUP B83B17000010001) and the TECNOMED project (funded by Regione Puglia, CUP B84I18000540002).
Funding Information:
A. Sierra also acknowledges the Academy of Finland (#275453) for financial support. A. Bravin acknowledges COST Action CA16122 (BIONECA) and CNR Italy for financial support. Finally M. Fratini , t t the COST Action CA16122 "Biomaterials and advanced physical techniques for regenerative cardiology and neurology is acknowledged for networking support.
Publisher Copyright:
© 2020
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/1
Y1 - 2021/1
N2 - PurposeImage processing plays a fundamental role in the study of central nervous system, for example in the analysis of the vascular network in neurodegenerative diseases. Synchrotron X-ray Phase-contrast micro-Tomography (SXPCT) is a very attractive method to study weakly absorbing samples and features, such as the vascular network in the spinal cord (SC). However, the identification and segmentation of vascular structures in SXPCT images is seriously hampered by the presence of image noise and strong contrast inhomogeneities, due to the sensitivity of the technique to small electronic density variations. In order to help with these tasks, we implemented a user-friendly ImageJ plugin based on a 3D Gaussian steerable filter, tuned up for the enhancement of tubular structures in SXPCT images.MethodsThe developed 3D Gaussian steerable filter plugin for ImageJ is based on the steerability properties of Gaussian derivatives. We applied it to SXPCT images of ex-vivo mouse SCs acquired at different experimental conditions.ResultsThe filter response shows a strong amplification of the source image contrast-to-background ratio (CBR), independently of structures orientation. We found that after the filter application, the CBR ratio increases by a factor ranging from ~6 to ~60. In addition, we also observed an increase of 35% of the contrast to noise ratio in the case of injured mouse SC.ConclusionThe developed tool can generally facilitate the detection/segmentation of capillaries, veins and arteries that were not clearly observable in non-filtered SXPCT images. Its systematic application could allow obtaining quantitative information from pre-clinical and clinical images.
AB - PurposeImage processing plays a fundamental role in the study of central nervous system, for example in the analysis of the vascular network in neurodegenerative diseases. Synchrotron X-ray Phase-contrast micro-Tomography (SXPCT) is a very attractive method to study weakly absorbing samples and features, such as the vascular network in the spinal cord (SC). However, the identification and segmentation of vascular structures in SXPCT images is seriously hampered by the presence of image noise and strong contrast inhomogeneities, due to the sensitivity of the technique to small electronic density variations. In order to help with these tasks, we implemented a user-friendly ImageJ plugin based on a 3D Gaussian steerable filter, tuned up for the enhancement of tubular structures in SXPCT images.MethodsThe developed 3D Gaussian steerable filter plugin for ImageJ is based on the steerability properties of Gaussian derivatives. We applied it to SXPCT images of ex-vivo mouse SCs acquired at different experimental conditions.ResultsThe filter response shows a strong amplification of the source image contrast-to-background ratio (CBR), independently of structures orientation. We found that after the filter application, the CBR ratio increases by a factor ranging from ~6 to ~60. In addition, we also observed an increase of 35% of the contrast to noise ratio in the case of injured mouse SC.ConclusionThe developed tool can generally facilitate the detection/segmentation of capillaries, veins and arteries that were not clearly observable in non-filtered SXPCT images. Its systematic application could allow obtaining quantitative information from pre-clinical and clinical images.
KW - 3D steerable filter
KW - Vascular network
KW - X ray phase contrast tomography
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U2 - 10.1016/j.ejmp.2020.12.010
DO - 10.1016/j.ejmp.2020.12.010
M3 - Article
AN - SCOPUS:85099464182
VL - 81
SP - 197
EP - 209
JO - Physica Medica
JF - Physica Medica
SN - 1120-1797
ER -