An Interactive Segmentation Tool for Quantifying Fat in Lumbar Muscles using Axial Lumbar-Spine MRI Article Swipe
Joseph Antony
,
Kevin McGuinness
,
Neil Welch
,
Joe Coyle
,
Andrew Franklyn‐Miller
,
Noel E. O’Connor
,
Kieran Moran
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1609.02744
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1609.02744
In this paper we present an interactive tool that can be used to quantify fat infiltration in lumbar muscles, which is useful in studying fat infiltration and lower back pain (LBP) in adults. Currently, a qualitative assessment by visual grading via a 5-point scale is used to study fat infiltration in lumbar muscles from an axial view of lumbar-spine MR Images. However, a quantitative approach (on a continuous scale of 0-100\%) may provide a greater insight. In this paper, we propose a method to precisely quantify the fat deposition / infiltration in a user-defined region of the lumbar muscles, which may aid better diagnosis and analysis. The key steps are interactively segmenting the region of interest (ROI) from the lumbar muscles using the well known livewire technique, identifying fatty regions in the segmented region based on variable-selection of threshold and softness levels, automatically detecting the center of the spinal column and fragmenting the lumbar muscles into smaller regions with reference to the center of the spinal column, computing key parameters [such as total and region-wise fat content percentage, total-cross sectional area (TCSA) and functional cross-sectional area (FCSA)] and exporting the computations and associated patient information from the MRI, into a database. A standalone application using MATLAB R2014a was developed to perform the required computations along with an intuitive graphical user interface (GUI).
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- http://doras.dcu.ie/20946/
- OA Status
- green
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3109562079
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3109562079Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.1609.02744Digital Object Identifier
- Title
-
An Interactive Segmentation Tool for Quantifying Fat in Lumbar Muscles using Axial Lumbar-Spine MRIWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2016Year of publication
- Publication date
-
2016-09-09Full publication date if available
- Authors
-
Joseph Antony, Kevin McGuinness, Neil Welch, Joe Coyle, Andrew Franklyn‐Miller, Noel E. O’Connor, Kieran MoranList of authors in order
- Landing page
-
https://doras.dcu.ie/20946/Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.48550/arxiv.1609.02744Direct OA link when available
- Concepts
-
Lumbar, Computer science, Segmentation, Spinal column, Grading (engineering), Lumbar spine, Biomedical engineering, Medicine, Artificial intelligence, Anatomy, Surgery, Biology, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
20Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3109562079 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.1609.02744 |
| ids.doi | https://doi.org/10.48550/arxiv.1609.02744 |
| ids.mag | 3109562079 |
| ids.openalex | https://openalex.org/W3109562079 |
| fwci | 0.0 |
| type | article |
| title | An Interactive Segmentation Tool for Quantifying Fat in Lumbar Muscles using Axial Lumbar-Spine MRI |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T14510 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9995999932289124 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2204 |
| topics[0].subfield.display_name | Biomedical Engineering |
| topics[0].display_name | Medical Imaging and Analysis |
| topics[1].id | https://openalex.org/T10238 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9815999865531921 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2734 |
| topics[1].subfield.display_name | Pathology and Forensic Medicine |
| topics[1].display_name | Spine and Intervertebral Disc Pathology |
| topics[2].id | https://openalex.org/T11227 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9768000245094299 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2712 |
| topics[2].subfield.display_name | Endocrinology, Diabetes and Metabolism |
| topics[2].display_name | Diabetic Foot Ulcer Assessment and Management |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C44575665 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7955973148345947 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1293405 |
| concepts[0].display_name | Lumbar |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5283518433570862 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C89600930 |
| concepts[2].level | 2 |
| concepts[2].score | 0.4748575687408447 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[2].display_name | Segmentation |
| concepts[3].id | https://openalex.org/C2993931576 |
| concepts[3].level | 2 |
| concepts[3].score | 0.4673768877983093 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q482853 |
| concepts[3].display_name | Spinal column |
| concepts[4].id | https://openalex.org/C2777286243 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4295378029346466 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q5591926 |
| concepts[4].display_name | Grading (engineering) |
| concepts[5].id | https://openalex.org/C3017433307 |
| concepts[5].level | 2 |
| concepts[5].score | 0.41632938385009766 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q405478 |
| concepts[5].display_name | Lumbar spine |
| concepts[6].id | https://openalex.org/C136229726 |
| concepts[6].level | 1 |
| concepts[6].score | 0.34721627831459045 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q327092 |
| concepts[6].display_name | Biomedical engineering |
| concepts[7].id | https://openalex.org/C71924100 |
| concepts[7].level | 0 |
| concepts[7].score | 0.2653999328613281 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[7].display_name | Medicine |
| concepts[8].id | https://openalex.org/C154945302 |
| concepts[8].level | 1 |
| concepts[8].score | 0.2616581916809082 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[8].display_name | Artificial intelligence |
| concepts[9].id | https://openalex.org/C105702510 |
| concepts[9].level | 1 |
| concepts[9].score | 0.259305477142334 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q514 |
| concepts[9].display_name | Anatomy |
| concepts[10].id | https://openalex.org/C141071460 |
| concepts[10].level | 1 |
| concepts[10].score | 0.09828653931617737 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q40821 |
| concepts[10].display_name | Surgery |
| concepts[11].id | https://openalex.org/C86803240 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0874844491481781 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[11].display_name | Biology |
| concepts[12].id | https://openalex.org/C18903297 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7150 |
| concepts[12].display_name | Ecology |
| keywords[0].id | https://openalex.org/keywords/lumbar |
| keywords[0].score | 0.7955973148345947 |
| keywords[0].display_name | Lumbar |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.5283518433570862 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/segmentation |
| keywords[2].score | 0.4748575687408447 |
| keywords[2].display_name | Segmentation |
| keywords[3].id | https://openalex.org/keywords/spinal-column |
| keywords[3].score | 0.4673768877983093 |
| keywords[3].display_name | Spinal column |
| keywords[4].id | https://openalex.org/keywords/grading |
| keywords[4].score | 0.4295378029346466 |
| keywords[4].display_name | Grading (engineering) |
| keywords[5].id | https://openalex.org/keywords/lumbar-spine |
| keywords[5].score | 0.41632938385009766 |
| keywords[5].display_name | Lumbar spine |
| keywords[6].id | https://openalex.org/keywords/biomedical-engineering |
| keywords[6].score | 0.34721627831459045 |
| keywords[6].display_name | Biomedical engineering |
| keywords[7].id | https://openalex.org/keywords/medicine |
| keywords[7].score | 0.2653999328613281 |
| keywords[7].display_name | Medicine |
| keywords[8].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[8].score | 0.2616581916809082 |
| keywords[8].display_name | Artificial intelligence |
| keywords[9].id | https://openalex.org/keywords/anatomy |
| keywords[9].score | 0.259305477142334 |
| keywords[9].display_name | Anatomy |
| keywords[10].id | https://openalex.org/keywords/surgery |
| keywords[10].score | 0.09828653931617737 |
| keywords[10].display_name | Surgery |
| keywords[11].id | https://openalex.org/keywords/biology |
| keywords[11].score | 0.0874844491481781 |
| keywords[11].display_name | Biology |
| language | en |
| locations[0].id | pmh:http://www.rian.ie/131951/ |
| locations[0].is_oa | False |
| locations[0].source | |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | submittedVersion |
| locations[0].raw_type | Other |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | Antony, Joseph ORCID: 0000-0001-6493-7829 <https://orcid.org/0000-0001-6493-7829>, McGuinness, Kevin ORCID: 0000-0003-1336-6477 <https://orcid.org/0000-0003-1336-6477>, Welch, Neil, Coyle, Joe, Franklyn-Miller, Andrew ORCID: 0000-0002-7826-2209 <https://orcid.org/0000-0002-7826-2209>, O'Connor, Noel E. ORCID: 0000-0002-4033-9135 <https://orcid.org/0000-0002-4033-9135> and Moran, Kieran ORCID: 0000-0003-2015-8967 <https://orcid.org/0000-0003-2015-8967> (2015) An interactive segmentation tool for quantifying fat in lumbar muscles using axial lumbar-spine MRI. IRBM, 37 (1). pp. 11-22. ISSN 1959-0318 |
| locations[0].landing_page_url | http://doras.dcu.ie/20946/ |
| locations[1].id | doi:10.48550/arxiv.1609.02744 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article-journal |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.1609.02744 |
| indexed_in | datacite |
| authorships[0].author.id | https://openalex.org/A5103043795 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Joseph Antony |
| authorships[0].countries | IE |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I42934936 |
| authorships[0].affiliations[0].raw_affiliation_string | Insight: Centre for Data Analytics, Dublin City University, Dublin, Ireland#TAB# |
| authorships[0].institutions[0].id | https://openalex.org/I42934936 |
| authorships[0].institutions[0].ror | https://ror.org/04a1a1e81 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I42934936 |
| authorships[0].institutions[0].country_code | IE |
| authorships[0].institutions[0].display_name | Dublin City University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Joseph Antony |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Insight: Centre for Data Analytics, Dublin City University, Dublin, Ireland#TAB# |
| authorships[1].author.id | https://openalex.org/A5073924795 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-1336-6477 |
| authorships[1].author.display_name | Kevin McGuinness |
| authorships[1].countries | IE |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I42934936 |
| authorships[1].affiliations[0].raw_affiliation_string | Insight: Centre for Data Analytics, Dublin City University, Dublin, Ireland#TAB# |
| authorships[1].institutions[0].id | https://openalex.org/I42934936 |
| authorships[1].institutions[0].ror | https://ror.org/04a1a1e81 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I42934936 |
| authorships[1].institutions[0].country_code | IE |
| authorships[1].institutions[0].display_name | Dublin City University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Kevin McGuinness |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Insight: Centre for Data Analytics, Dublin City University, Dublin, Ireland#TAB# |
| authorships[2].author.id | https://openalex.org/A5020922768 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-6678-6198 |
| authorships[2].author.display_name | Neil Welch |
| authorships[2].countries | IE |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210150414 |
| authorships[2].affiliations[0].raw_affiliation_string | Sports Medicine Department, Sports Surgery Clinic, Dublin, Ireland |
| authorships[2].institutions[0].id | https://openalex.org/I4210150414 |
| authorships[2].institutions[0].ror | https://ror.org/05cga6x45 |
| authorships[2].institutions[0].type | healthcare |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210150414 |
| authorships[2].institutions[0].country_code | IE |
| authorships[2].institutions[0].display_name | Sports Surgery Clinic |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Neil Welch |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Sports Medicine Department, Sports Surgery Clinic, Dublin, Ireland |
| authorships[3].author.id | https://openalex.org/A5075705054 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Joe Coyle |
| authorships[3].countries | IE |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210150414 |
| authorships[3].affiliations[0].raw_affiliation_string | Sports Medicine Department, Sports Surgery Clinic, Dublin, Ireland |
| authorships[3].institutions[0].id | https://openalex.org/I4210150414 |
| authorships[3].institutions[0].ror | https://ror.org/05cga6x45 |
| authorships[3].institutions[0].type | healthcare |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210150414 |
| authorships[3].institutions[0].country_code | IE |
| authorships[3].institutions[0].display_name | Sports Surgery Clinic |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Joe Coyle |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Sports Medicine Department, Sports Surgery Clinic, Dublin, Ireland |
| authorships[4].author.id | https://openalex.org/A5057232230 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-7826-2209 |
| authorships[4].author.display_name | Andrew Franklyn‐Miller |
| authorships[4].countries | IE |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210150414 |
| authorships[4].affiliations[0].raw_affiliation_string | Sports Medicine Department, Sports Surgery Clinic, Dublin, Ireland |
| authorships[4].institutions[0].id | https://openalex.org/I4210150414 |
| authorships[4].institutions[0].ror | https://ror.org/05cga6x45 |
| authorships[4].institutions[0].type | healthcare |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210150414 |
| authorships[4].institutions[0].country_code | IE |
| authorships[4].institutions[0].display_name | Sports Surgery Clinic |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Andrew Franklyn-Miller |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Sports Medicine Department, Sports Surgery Clinic, Dublin, Ireland |
| authorships[5].author.id | https://openalex.org/A5106498523 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-4033-9135 |
| authorships[5].author.display_name | Noel E. O’Connor |
| authorships[5].countries | IE |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I42934936 |
| authorships[5].affiliations[0].raw_affiliation_string | Insight: Centre for Data Analytics, Dublin City University, Dublin, Ireland#TAB# |
| authorships[5].institutions[0].id | https://openalex.org/I42934936 |
| authorships[5].institutions[0].ror | https://ror.org/04a1a1e81 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I42934936 |
| authorships[5].institutions[0].country_code | IE |
| authorships[5].institutions[0].display_name | Dublin City University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Noel E. O'Connor |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Insight: Centre for Data Analytics, Dublin City University, Dublin, Ireland#TAB# |
| authorships[6].author.id | https://openalex.org/A5017965675 |
| authorships[6].author.orcid | https://orcid.org/0000-0003-2015-8967 |
| authorships[6].author.display_name | Kieran Moran |
| authorships[6].countries | IE |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I42934936 |
| authorships[6].affiliations[0].raw_affiliation_string | School of Health and Human Performance, Dublin City University, Dublin, Ireland. |
| authorships[6].institutions[0].id | https://openalex.org/I42934936 |
| authorships[6].institutions[0].ror | https://ror.org/04a1a1e81 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I42934936 |
| authorships[6].institutions[0].country_code | IE |
| authorships[6].institutions[0].display_name | Dublin City University |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Kieran Moran |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | School of Health and Human Performance, Dublin City University, Dublin, Ireland. |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.48550/arxiv.1609.02744 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | An Interactive Segmentation Tool for Quantifying Fat in Lumbar Muscles using Axial Lumbar-Spine MRI |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T14510 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9995999932289124 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2204 |
| primary_topic.subfield.display_name | Biomedical Engineering |
| primary_topic.display_name | Medical Imaging and Analysis |
| related_works | https://openalex.org/W2952750045, https://openalex.org/W3208771569, https://openalex.org/W2033365879, https://openalex.org/W2361231651, https://openalex.org/W3030881019, https://openalex.org/W2038251514, https://openalex.org/W2769434453, https://openalex.org/W2376545611, https://openalex.org/W2128804547, https://openalex.org/W1821795989, https://openalex.org/W2806959783, https://openalex.org/W3124189820, https://openalex.org/W2082080708, https://openalex.org/W3096765115, https://openalex.org/W2402758574, https://openalex.org/W2063135761, https://openalex.org/W2293786381, https://openalex.org/W561397323, https://openalex.org/W1967799757, https://openalex.org/W2791169768 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.48550/arxiv.1609.02744 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | |
| best_oa_location.raw_type | article-journal |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.48550/arxiv.1609.02744 |
| primary_location.id | pmh:http://www.rian.ie/131951/ |
| primary_location.is_oa | False |
| primary_location.source | |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | submittedVersion |
| primary_location.raw_type | Other |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | Antony, Joseph ORCID: 0000-0001-6493-7829 <https://orcid.org/0000-0001-6493-7829>, McGuinness, Kevin ORCID: 0000-0003-1336-6477 <https://orcid.org/0000-0003-1336-6477>, Welch, Neil, Coyle, Joe, Franklyn-Miller, Andrew ORCID: 0000-0002-7826-2209 <https://orcid.org/0000-0002-7826-2209>, O'Connor, Noel E. ORCID: 0000-0002-4033-9135 <https://orcid.org/0000-0002-4033-9135> and Moran, Kieran ORCID: 0000-0003-2015-8967 <https://orcid.org/0000-0003-2015-8967> (2015) An interactive segmentation tool for quantifying fat in lumbar muscles using axial lumbar-spine MRI. IRBM, 37 (1). pp. 11-22. ISSN 1959-0318 |
| primary_location.landing_page_url | http://doras.dcu.ie/20946/ |
| publication_date | 2016-09-09 |
| publication_year | 2016 |
| referenced_works_count | 0 |
| abstract_inverted_index./ | 89 |
| abstract_inverted_index.A | 201 |
| abstract_inverted_index.a | 34, 41, 62, 66, 73, 81, 92, 199 |
| abstract_inverted_index.In | 0, 76 |
| abstract_inverted_index.MR | 59 |
| abstract_inverted_index.an | 5, 54, 216 |
| abstract_inverted_index.as | 171 |
| abstract_inverted_index.be | 10 |
| abstract_inverted_index.by | 37 |
| abstract_inverted_index.in | 16, 22, 31, 50, 91, 130 |
| abstract_inverted_index.is | 20, 44 |
| abstract_inverted_index.of | 57, 69, 95, 114, 137, 146, 163 |
| abstract_inverted_index.on | 135 |
| abstract_inverted_index.to | 12, 46, 83, 160, 209 |
| abstract_inverted_index.we | 3, 79 |
| abstract_inverted_index.(on | 65 |
| abstract_inverted_index.The | 106 |
| abstract_inverted_index.aid | 101 |
| abstract_inverted_index.and | 26, 104, 139, 150, 173, 182, 187, 191 |
| abstract_inverted_index.are | 109 |
| abstract_inverted_index.can | 9 |
| abstract_inverted_index.fat | 14, 24, 48, 87, 175 |
| abstract_inverted_index.key | 107, 168 |
| abstract_inverted_index.may | 71, 100 |
| abstract_inverted_index.the | 86, 96, 112, 118, 122, 131, 144, 147, 152, 161, 164, 189, 196, 211 |
| abstract_inverted_index.via | 40 |
| abstract_inverted_index.was | 207 |
| abstract_inverted_index.MRI, | 197 |
| abstract_inverted_index.area | 180, 185 |
| abstract_inverted_index.back | 28 |
| abstract_inverted_index.from | 53, 117, 195 |
| abstract_inverted_index.into | 155, 198 |
| abstract_inverted_index.pain | 29 |
| abstract_inverted_index.that | 8 |
| abstract_inverted_index.this | 1, 77 |
| abstract_inverted_index.tool | 7 |
| abstract_inverted_index.used | 11, 45 |
| abstract_inverted_index.user | 219 |
| abstract_inverted_index.view | 56 |
| abstract_inverted_index.well | 123 |
| abstract_inverted_index.with | 158, 215 |
| abstract_inverted_index.(LBP) | 30 |
| abstract_inverted_index.(ROI) | 116 |
| abstract_inverted_index.[such | 170 |
| abstract_inverted_index.along | 214 |
| abstract_inverted_index.axial | 55 |
| abstract_inverted_index.based | 134 |
| abstract_inverted_index.fatty | 128 |
| abstract_inverted_index.known | 124 |
| abstract_inverted_index.lower | 27 |
| abstract_inverted_index.paper | 2 |
| abstract_inverted_index.scale | 43, 68 |
| abstract_inverted_index.steps | 108 |
| abstract_inverted_index.study | 47 |
| abstract_inverted_index.total | 172 |
| abstract_inverted_index.using | 121, 204 |
| abstract_inverted_index.which | 19, 99 |
| abstract_inverted_index.(GUI). | 221 |
| abstract_inverted_index.(TCSA) | 181 |
| abstract_inverted_index.MATLAB | 205 |
| abstract_inverted_index.R2014a | 206 |
| abstract_inverted_index.better | 102 |
| abstract_inverted_index.center | 145, 162 |
| abstract_inverted_index.column | 149 |
| abstract_inverted_index.lumbar | 17, 51, 97, 119, 153 |
| abstract_inverted_index.method | 82 |
| abstract_inverted_index.paper, | 78 |
| abstract_inverted_index.region | 94, 113, 133 |
| abstract_inverted_index.spinal | 148, 165 |
| abstract_inverted_index.useful | 21 |
| abstract_inverted_index.visual | 38 |
| abstract_inverted_index.(FCSA)] | 186 |
| abstract_inverted_index.5-point | 42 |
| abstract_inverted_index.Images. | 60 |
| abstract_inverted_index.adults. | 32 |
| abstract_inverted_index.column, | 166 |
| abstract_inverted_index.content | 176 |
| abstract_inverted_index.grading | 39 |
| abstract_inverted_index.greater | 74 |
| abstract_inverted_index.levels, | 141 |
| abstract_inverted_index.muscles | 52, 120, 154 |
| abstract_inverted_index.patient | 193 |
| abstract_inverted_index.perform | 210 |
| abstract_inverted_index.present | 4 |
| abstract_inverted_index.propose | 80 |
| abstract_inverted_index.provide | 72 |
| abstract_inverted_index.regions | 129, 157 |
| abstract_inverted_index.smaller | 156 |
| abstract_inverted_index.0-100\%) | 70 |
| abstract_inverted_index.However, | 61 |
| abstract_inverted_index.approach | 64 |
| abstract_inverted_index.insight. | 75 |
| abstract_inverted_index.interest | 115 |
| abstract_inverted_index.livewire | 125 |
| abstract_inverted_index.muscles, | 18, 98 |
| abstract_inverted_index.quantify | 13, 85 |
| abstract_inverted_index.required | 212 |
| abstract_inverted_index.softness | 140 |
| abstract_inverted_index.studying | 23 |
| abstract_inverted_index.analysis. | 105 |
| abstract_inverted_index.computing | 167 |
| abstract_inverted_index.database. | 200 |
| abstract_inverted_index.detecting | 143 |
| abstract_inverted_index.developed | 208 |
| abstract_inverted_index.diagnosis | 103 |
| abstract_inverted_index.exporting | 188 |
| abstract_inverted_index.graphical | 218 |
| abstract_inverted_index.interface | 220 |
| abstract_inverted_index.intuitive | 217 |
| abstract_inverted_index.precisely | 84 |
| abstract_inverted_index.reference | 159 |
| abstract_inverted_index.sectional | 179 |
| abstract_inverted_index.segmented | 132 |
| abstract_inverted_index.threshold | 138 |
| abstract_inverted_index.Currently, | 33 |
| abstract_inverted_index.assessment | 36 |
| abstract_inverted_index.associated | 192 |
| abstract_inverted_index.continuous | 67 |
| abstract_inverted_index.deposition | 88 |
| abstract_inverted_index.functional | 183 |
| abstract_inverted_index.parameters | 169 |
| abstract_inverted_index.segmenting | 111 |
| abstract_inverted_index.standalone | 202 |
| abstract_inverted_index.technique, | 126 |
| abstract_inverted_index.application | 203 |
| abstract_inverted_index.fragmenting | 151 |
| abstract_inverted_index.identifying | 127 |
| abstract_inverted_index.information | 194 |
| abstract_inverted_index.interactive | 6 |
| abstract_inverted_index.percentage, | 177 |
| abstract_inverted_index.qualitative | 35 |
| abstract_inverted_index.region-wise | 174 |
| abstract_inverted_index.total-cross | 178 |
| abstract_inverted_index.computations | 190, 213 |
| abstract_inverted_index.infiltration | 15, 25, 49, 90 |
| abstract_inverted_index.lumbar-spine | 58 |
| abstract_inverted_index.quantitative | 63 |
| abstract_inverted_index.user-defined | 93 |
| abstract_inverted_index.automatically | 142 |
| abstract_inverted_index.interactively | 110 |
| abstract_inverted_index.cross-sectional | 184 |
| abstract_inverted_index.variable-selection | 136 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.25124049 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |