Morphological image processing for multiscale analysis of super-resolution ultrasound images of tissue microvascular networks

Ipek Özdemir, Kenneth Hoyt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Diabetes is a major disease and known to impair microvascular recruitment due to insulin resistance. Previous quantifications of the changes in microvascular networks at the capillary level were being performed with either full or manually selected region-of-interests (ROIs) from super-resolution ultrasound (SR-US) images. However, these approaches were imprecise, time-consuming, and unsuitable for automated processes. Here we provided a custom software solution for automated multiscale analysis of SR-US images of tissue microvascularity patterns. An Acuson Sequoia 512 ultrasound (US) scanner equipped with a 15L8-S linear array transducer was used in a nonlinear imaging mode to collect all data. C57BL/6J male mice fed standard chow and studied at age 13-16 wk comprised the lean group (N = 14), and 24-31 wk-old mice who received a high-fat diet provided the obese group (N = 8). After administration of a microbubble (MB) contrast agent, the proximal hindlimb adductor muscle of each animal was imaged (dynamic contrast-enhanced US, DCE-US) for 10 min at baseline and again at 1 h and towards the end of a 2 h hyperinsulinemiceuglycemic clamp. Vascular structures were enhanced with a multiscale vessel enhancement filter and binary vessel segments were delineated using Otsu's global threshold method. We then computed vessel diameters by employing morphological image processing methods for quantitative analysis. Our custom software enabled automated multiscale image examination by defining a diameter threshold to limit the analysis at the capillary level. Longitudinal changes in AUC, IPK, and MVD were significant for lean group (p < 0.02 using Full-ROI and p < 0.01 using 150 μm-ROI) and for obese group (p < 0.02 using Full-ROI, p < 0.03 using 150 μm-ROI). By eliminating large vessels from the ROI (above 150 μm in diameter), perfusion parameters were more sensitive to changes exhibited by the smaller vessels, that are known to be more impacted by disease and treatment.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationUltrasonic Imaging and Tomography
EditorsBrett C. Byram, Nicole V. Ruiter
PublisherSPIE
ISBN (Electronic)9781510625570
DOIs
StatePublished - Jan 1 2019
EventMedical Imaging 2019: Ultrasonic Imaging and Tomography - San Diego, United States
Duration: Feb 17 2019Feb 18 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10955
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Ultrasonic Imaging and Tomography
CountryUnited States
CitySan Diego
Period2/17/192/18/19

Fingerprint

image resolution
Microvessels
vessels
image processing
Image processing
Sequoia
Software
Ultrasonics
Tissue
Microbubbles
Public Opinion
High Fat Diet
Hindlimb
Transducers
Contrast Media
Area Under Curve
Blood Vessels
Insulin Resistance
Perfusion
mice

Keywords

  • Diabetes
  • Microvascular networks
  • Morphological image processing
  • Multiscale vessel enhancement
  • Perfusion parameters
  • Restricted ROI
  • Skeletal muscle tissue
  • SR-US imaging

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Özdemir, I., & Hoyt, K. (2019). Morphological image processing for multiscale analysis of super-resolution ultrasound images of tissue microvascular networks. In B. C. Byram, & N. V. Ruiter (Eds.), Medical Imaging 2019: Ultrasonic Imaging and Tomography [1095505] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10955). SPIE. https://doi.org/10.1117/12.2511974

Morphological image processing for multiscale analysis of super-resolution ultrasound images of tissue microvascular networks. / Özdemir, Ipek; Hoyt, Kenneth.

Medical Imaging 2019: Ultrasonic Imaging and Tomography. ed. / Brett C. Byram; Nicole V. Ruiter. SPIE, 2019. 1095505 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10955).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Özdemir, I & Hoyt, K 2019, Morphological image processing for multiscale analysis of super-resolution ultrasound images of tissue microvascular networks. in BC Byram & NV Ruiter (eds), Medical Imaging 2019: Ultrasonic Imaging and Tomography., 1095505, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10955, SPIE, Medical Imaging 2019: Ultrasonic Imaging and Tomography, San Diego, United States, 2/17/19. https://doi.org/10.1117/12.2511974
Özdemir I, Hoyt K. Morphological image processing for multiscale analysis of super-resolution ultrasound images of tissue microvascular networks. In Byram BC, Ruiter NV, editors, Medical Imaging 2019: Ultrasonic Imaging and Tomography. SPIE. 2019. 1095505. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2511974
Özdemir, Ipek ; Hoyt, Kenneth. / Morphological image processing for multiscale analysis of super-resolution ultrasound images of tissue microvascular networks. Medical Imaging 2019: Ultrasonic Imaging and Tomography. editor / Brett C. Byram ; Nicole V. Ruiter. SPIE, 2019. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
@inproceedings{369c5320c40a49d08b8787b82a4fd65e,
title = "Morphological image processing for multiscale analysis of super-resolution ultrasound images of tissue microvascular networks",
abstract = "Diabetes is a major disease and known to impair microvascular recruitment due to insulin resistance. Previous quantifications of the changes in microvascular networks at the capillary level were being performed with either full or manually selected region-of-interests (ROIs) from super-resolution ultrasound (SR-US) images. However, these approaches were imprecise, time-consuming, and unsuitable for automated processes. Here we provided a custom software solution for automated multiscale analysis of SR-US images of tissue microvascularity patterns. An Acuson Sequoia 512 ultrasound (US) scanner equipped with a 15L8-S linear array transducer was used in a nonlinear imaging mode to collect all data. C57BL/6J male mice fed standard chow and studied at age 13-16 wk comprised the lean group (N = 14), and 24-31 wk-old mice who received a high-fat diet provided the obese group (N = 8). After administration of a microbubble (MB) contrast agent, the proximal hindlimb adductor muscle of each animal was imaged (dynamic contrast-enhanced US, DCE-US) for 10 min at baseline and again at 1 h and towards the end of a 2 h hyperinsulinemiceuglycemic clamp. Vascular structures were enhanced with a multiscale vessel enhancement filter and binary vessel segments were delineated using Otsu's global threshold method. We then computed vessel diameters by employing morphological image processing methods for quantitative analysis. Our custom software enabled automated multiscale image examination by defining a diameter threshold to limit the analysis at the capillary level. Longitudinal changes in AUC, IPK, and MVD were significant for lean group (p < 0.02 using Full-ROI and p < 0.01 using 150 μm-ROI) and for obese group (p < 0.02 using Full-ROI, p < 0.03 using 150 μm-ROI). By eliminating large vessels from the ROI (above 150 μm in diameter), perfusion parameters were more sensitive to changes exhibited by the smaller vessels, that are known to be more impacted by disease and treatment.",
keywords = "Diabetes, Microvascular networks, Morphological image processing, Multiscale vessel enhancement, Perfusion parameters, Restricted ROI, Skeletal muscle tissue, SR-US imaging",
author = "Ipek {\"O}zdemir and Kenneth Hoyt",
year = "2019",
month = "1",
day = "1",
doi = "10.1117/12.2511974",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Byram, {Brett C.} and Ruiter, {Nicole V.}",
booktitle = "Medical Imaging 2019",

}

TY - GEN

T1 - Morphological image processing for multiscale analysis of super-resolution ultrasound images of tissue microvascular networks

AU - Özdemir, Ipek

AU - Hoyt, Kenneth

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Diabetes is a major disease and known to impair microvascular recruitment due to insulin resistance. Previous quantifications of the changes in microvascular networks at the capillary level were being performed with either full or manually selected region-of-interests (ROIs) from super-resolution ultrasound (SR-US) images. However, these approaches were imprecise, time-consuming, and unsuitable for automated processes. Here we provided a custom software solution for automated multiscale analysis of SR-US images of tissue microvascularity patterns. An Acuson Sequoia 512 ultrasound (US) scanner equipped with a 15L8-S linear array transducer was used in a nonlinear imaging mode to collect all data. C57BL/6J male mice fed standard chow and studied at age 13-16 wk comprised the lean group (N = 14), and 24-31 wk-old mice who received a high-fat diet provided the obese group (N = 8). After administration of a microbubble (MB) contrast agent, the proximal hindlimb adductor muscle of each animal was imaged (dynamic contrast-enhanced US, DCE-US) for 10 min at baseline and again at 1 h and towards the end of a 2 h hyperinsulinemiceuglycemic clamp. Vascular structures were enhanced with a multiscale vessel enhancement filter and binary vessel segments were delineated using Otsu's global threshold method. We then computed vessel diameters by employing morphological image processing methods for quantitative analysis. Our custom software enabled automated multiscale image examination by defining a diameter threshold to limit the analysis at the capillary level. Longitudinal changes in AUC, IPK, and MVD were significant for lean group (p < 0.02 using Full-ROI and p < 0.01 using 150 μm-ROI) and for obese group (p < 0.02 using Full-ROI, p < 0.03 using 150 μm-ROI). By eliminating large vessels from the ROI (above 150 μm in diameter), perfusion parameters were more sensitive to changes exhibited by the smaller vessels, that are known to be more impacted by disease and treatment.

AB - Diabetes is a major disease and known to impair microvascular recruitment due to insulin resistance. Previous quantifications of the changes in microvascular networks at the capillary level were being performed with either full or manually selected region-of-interests (ROIs) from super-resolution ultrasound (SR-US) images. However, these approaches were imprecise, time-consuming, and unsuitable for automated processes. Here we provided a custom software solution for automated multiscale analysis of SR-US images of tissue microvascularity patterns. An Acuson Sequoia 512 ultrasound (US) scanner equipped with a 15L8-S linear array transducer was used in a nonlinear imaging mode to collect all data. C57BL/6J male mice fed standard chow and studied at age 13-16 wk comprised the lean group (N = 14), and 24-31 wk-old mice who received a high-fat diet provided the obese group (N = 8). After administration of a microbubble (MB) contrast agent, the proximal hindlimb adductor muscle of each animal was imaged (dynamic contrast-enhanced US, DCE-US) for 10 min at baseline and again at 1 h and towards the end of a 2 h hyperinsulinemiceuglycemic clamp. Vascular structures were enhanced with a multiscale vessel enhancement filter and binary vessel segments were delineated using Otsu's global threshold method. We then computed vessel diameters by employing morphological image processing methods for quantitative analysis. Our custom software enabled automated multiscale image examination by defining a diameter threshold to limit the analysis at the capillary level. Longitudinal changes in AUC, IPK, and MVD were significant for lean group (p < 0.02 using Full-ROI and p < 0.01 using 150 μm-ROI) and for obese group (p < 0.02 using Full-ROI, p < 0.03 using 150 μm-ROI). By eliminating large vessels from the ROI (above 150 μm in diameter), perfusion parameters were more sensitive to changes exhibited by the smaller vessels, that are known to be more impacted by disease and treatment.

KW - Diabetes

KW - Microvascular networks

KW - Morphological image processing

KW - Multiscale vessel enhancement

KW - Perfusion parameters

KW - Restricted ROI

KW - Skeletal muscle tissue

KW - SR-US imaging

UR - http://www.scopus.com/inward/record.url?scp=85066607423&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85066607423&partnerID=8YFLogxK

U2 - 10.1117/12.2511974

DO - 10.1117/12.2511974

M3 - Conference contribution

AN - SCOPUS:85066607423

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2019

A2 - Byram, Brett C.

A2 - Ruiter, Nicole V.

PB - SPIE

ER -