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Automatic segmentat...
Automatic segmentation of large-scale CT image datasets for detailed body composition analysis
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- Ahmad, Nouman (författare)
- Uppsala universitet,Radiologi
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- Strand, Robin, 1978- (författare)
- Uppsala universitet,Radiologi,Bildanalys och människa-datorinteraktion
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- Sparresäter, Björn (författare)
- Uppsala universitet,Radiologi
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- Tarai, Sambit (författare)
- Uppsala universitet,Radiologi
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- Lundström, Elin (författare)
- Uppsala universitet,Radiologi
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- Bergström, Göran, 1964 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine
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- Ahlström, Håkan, 1953- (författare)
- Uppsala universitet,Radiologi
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- Kullberg, Joel, 1979- (författare)
- Uppsala universitet,Radiologi
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(creator_code:org_t)
- BioMed Central (BMC), 2023
- 2023
- Engelska.
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Ingår i: BMC BIOINFORMATICS. - : BioMed Central (BMC). - 1471-2105. ; 24:1
- Relaterad länk:
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https://doi.org/10.1...
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https://uu.diva-port... (primary) (Raw object)
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https://gup.ub.gu.se...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- BackgroundBody composition (BC) is an important factor in determining the risk of type 2-diabetes and cardiovascular disease. Computed tomography (CT) is a useful imaging technique for studying BC, however manual segmentation of CT images is time-consuming and subjective. The purpose of this study is to develop and evaluate fully automated segmentation techniques applicable to a 3-slice CT imaging protocol, consisting of single slices at the level of the liver, abdomen, and thigh, allowing detailed analysis of numerous tissues and organs.MethodsThe study used more than 4000 CT subjects acquired from the large-scale SCAPIS and IGT cohort to train and evaluate four convolutional neural network based architectures: ResUNET, UNET++, Ghost-UNET, and the proposed Ghost-UNET++. The segmentation techniques were developed and evaluated for automated segmentation of the liver, spleen, skeletal muscle, bone marrow, cortical bone, and various adipose tissue depots, including visceral (VAT), intraperitoneal (IPAT), retroperitoneal (RPAT), subcutaneous (SAT), deep (DSAT), and superficial SAT (SSAT), as well as intermuscular adipose tissue (IMAT). The models were trained and validated for each target using tenfold cross-validation and test sets.ResultsThe Dice scores on cross validation in SCAPIS were: ResUNET 0.964 (0.909-0.996), UNET++ 0.981 (0.927-0.996), Ghost-UNET 0.961 (0.904-0.991), and Ghost-UNET++ 0.968 (0.910-0.994). All four models showed relatively strong results, however UNET++ had the best performance overall. Ghost-UNET++ performed competitively compared to UNET++ and showed a more computationally efficient approach.ConclusionFully automated segmentation techniques can be successfully applied to a 3-slice CT imaging protocol to analyze multiple tissues and organs related to BC. The overall best performance was achieved by UNET++, against which Ghost-UNET++ showed competitive results based on a more computationally efficient approach. The use of fully automated segmentation methods can reduce analysis time and provide objective results in large-scale studies of BC.
Ämnesord
- NATURVETENSKAP -- Biologi -- Biokemi och molekylärbiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Biochemistry and Molecular Biology (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Nyckelord
- Deep learning
- Segmentation
- Medical imaging
- Computed tomography
- Body composition
- Body composition
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Ahmad, Nouman
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Strand, Robin, 1 ...
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Sparresäter, Bjö ...
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Tarai, Sambit
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Lundström, Elin
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Bergström, Göran ...
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visa fler...
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Ahlström, Håkan, ...
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Kullberg, Joel, ...
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visa färre...
- Om ämnet
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- NATURVETENSKAP
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NATURVETENSKAP
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och Biologi
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och Biokemi och mole ...
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Klinisk medicin
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och Radiologi och bi ...
- Artiklar i publikationen
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BMC BIOINFORMATI ...
- Av lärosätet
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Göteborgs universitet
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Uppsala universitet