Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment

Objective To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segmentations as the reference standard. Materials and m...

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Published inSkeletal radiology Vol. 49; no. 3; pp. 387 - 395
Main Authors Hemke, Robert, Buckless, Colleen G., Tsao, Andrew, Wang, Benjamin, Torriani, Martin
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2020
Springer
Springer Nature B.V
Subjects
Acetabulum
Adipose tissue
Adipose Tissue - diagnostic imaging
Adipose tissues
Artificial neural networks
Automation
Body Composition
Bone composition
Computed tomography
Contrast Media
CT imaging
Data augmentation
Datasets
Datasets as Topic
Deep learning
Equalization
Female
Histograms
Humans
Image processing
Image segmentation
Imaging
Male
Medical imaging equipment
Medicine
Medicine & Public Health
Middle Aged
Muscle, Skeletal - diagnostic imaging
Muscles
Neural networks
Neural Networks, Computer
Nuclear Medicine
Orthopedics
Pathology
Pelvis
Pelvis - diagnostic imaging
Physiological aspects
Radiology
Retrospective Studies
Scientific Article
Tomography, X-Ray Computed
Training
Deep learning
Muscle
Segmentation
Computed tomography
Pelvis
Body composition
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Abstract Objective To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segmentations as the reference standard. Materials and methods We manually segmented 200 axial CT images at the supra-acetabular level in 200 subjects, labeling background, subcutaneous adipose tissue (SAT), muscle, inter-muscular adipose tissue (IMAT), bone, and miscellaneous intra-pelvic content. The dataset was randomly divided into training (180/200) and test (20/200) datasets. Data augmentation was utilized to enlarge the training dataset and all images underwent preprocessing with histogram equalization. Our model was trained for 50 epochs using the U-Net architecture with batch size of 8, learning rate of 0.0001, Adadelta optimizer and a dropout of 0.20. The Dice (F1) score was used to assess similarity between the manual segmentations and the CNN predicted segmentations. Results The CNN model with data augmentation of N  = 3000 achieved accurate segmentation of body composition for all classes. The Dice scores were as follows: background (1.00), miscellaneous intra-pelvic content (0.98), SAT (0.97), muscle (0.95), IMAT (0.91), and bone (0.92). Mean time to automatically segment one CT image was 0.07 s (GPU) and 2.51 s (CPU). Conclusions Our CNN-based model enables accurate automated segmentation of multiple tissues on pelvic CT images, with promising implications for body composition studies.
AbstractList Objective To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segmentations as the reference standard. Materials and methods We manually segmented 200 axial CT images at the supra-acetabular level in 200 subjects, labeling background, subcutaneous adipose tissue (SAT), muscle, inter-muscular adipose tissue (IMAT), bone, and miscellaneous intra-pelvic content. The dataset was randomly divided into training (180/200) and test (20/200) datasets. Data augmentation was utilized to enlarge the training dataset and all images underwent preprocessing with histogram equalization. Our model was trained for 50 epochs using the U-Net architecture with batch size of 8, learning rate of 0.0001, Adadelta optimizer and a dropout of 0.20. The Dice (F1) score was used to assess similarity between the manual segmentations and the CNN predicted segmentations. Results The CNN model with data augmentation of N = 3000 achieved accurate segmentation of body composition for all classes. The Dice scores were as follows: background (1.00), miscellaneous intra-pelvic content (0.98), SAT (0.97), muscle (0.95), IMAT (0.91), and bone (0.92). Mean time to automatically segment one CT image was 0.07 s (GPU) and 2.51 s (CPU). Conclusions Our CNN-based model enables accurate automated segmentation of multiple tissues on pelvic CT images, with promising implications for body composition studies.
To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segmentations as the reference standard.OBJECTIVETo develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segmentations as the reference standard.We manually segmented 200 axial CT images at the supra-acetabular level in 200 subjects, labeling background, subcutaneous adipose tissue (SAT), muscle, inter-muscular adipose tissue (IMAT), bone, and miscellaneous intra-pelvic content. The dataset was randomly divided into training (180/200) and test (20/200) datasets. Data augmentation was utilized to enlarge the training dataset and all images underwent preprocessing with histogram equalization. Our model was trained for 50 epochs using the U-Net architecture with batch size of 8, learning rate of 0.0001, Adadelta optimizer and a dropout of 0.20. The Dice (F1) score was used to assess similarity between the manual segmentations and the CNN predicted segmentations.MATERIALS AND METHODSWe manually segmented 200 axial CT images at the supra-acetabular level in 200 subjects, labeling background, subcutaneous adipose tissue (SAT), muscle, inter-muscular adipose tissue (IMAT), bone, and miscellaneous intra-pelvic content. The dataset was randomly divided into training (180/200) and test (20/200) datasets. Data augmentation was utilized to enlarge the training dataset and all images underwent preprocessing with histogram equalization. Our model was trained for 50 epochs using the U-Net architecture with batch size of 8, learning rate of 0.0001, Adadelta optimizer and a dropout of 0.20. The Dice (F1) score was used to assess similarity between the manual segmentations and the CNN predicted segmentations.The CNN model with data augmentation of N = 3000 achieved accurate segmentation of body composition for all classes. The Dice scores were as follows: background (1.00), miscellaneous intra-pelvic content (0.98), SAT (0.97), muscle (0.95), IMAT (0.91), and bone (0.92). Mean time to automatically segment one CT image was 0.07 s (GPU) and 2.51 s (CPU).RESULTSThe CNN model with data augmentation of N = 3000 achieved accurate segmentation of body composition for all classes. The Dice scores were as follows: background (1.00), miscellaneous intra-pelvic content (0.98), SAT (0.97), muscle (0.95), IMAT (0.91), and bone (0.92). Mean time to automatically segment one CT image was 0.07 s (GPU) and 2.51 s (CPU).Our CNN-based model enables accurate automated segmentation of multiple tissues on pelvic CT images, with promising implications for body composition studies.CONCLUSIONSOur CNN-based model enables accurate automated segmentation of multiple tissues on pelvic CT images, with promising implications for body composition studies.
ObjectiveTo develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segmentations as the reference standard.Materials and methodsWe manually segmented 200 axial CT images at the supra-acetabular level in 200 subjects, labeling background, subcutaneous adipose tissue (SAT), muscle, inter-muscular adipose tissue (IMAT), bone, and miscellaneous intra-pelvic content. The dataset was randomly divided into training (180/200) and test (20/200) datasets. Data augmentation was utilized to enlarge the training dataset and all images underwent preprocessing with histogram equalization. Our model was trained for 50 epochs using the U-Net architecture with batch size of 8, learning rate of 0.0001, Adadelta optimizer and a dropout of 0.20. The Dice (F1) score was used to assess similarity between the manual segmentations and the CNN predicted segmentations.ResultsThe CNN model with data augmentation of N = 3000 achieved accurate segmentation of body composition for all classes. The Dice scores were as follows: background (1.00), miscellaneous intra-pelvic content (0.98), SAT (0.97), muscle (0.95), IMAT (0.91), and bone (0.92). Mean time to automatically segment one CT image was 0.07 s (GPU) and 2.51 s (CPU).ConclusionsOur CNN-based model enables accurate automated segmentation of multiple tissues on pelvic CT images, with promising implications for body composition studies.
To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segmentations as the reference standard. We manually segmented 200 axial CT images at the supra-acetabular level in 200 subjects, labeling background, subcutaneous adipose tissue (SAT), muscle, inter-muscular adipose tissue (IMAT), bone, and miscellaneous intra-pelvic content. The dataset was randomly divided into training (180/200) and test (20/200) datasets. Data augmentation was utilized to enlarge the training dataset and all images underwent preprocessing with histogram equalization. Our model was trained for 50 epochs using the U-Net architecture with batch size of 8, learning rate of 0.0001, Adadelta optimizer and a dropout of 0.20. The Dice (F1) score was used to assess similarity between the manual segmentations and the CNN predicted segmentations. The CNN model with data augmentation of N = 3000 achieved accurate segmentation of body composition for all classes. The Dice scores were as follows: background (1.00), miscellaneous intra-pelvic content (0.98), SAT (0.97), muscle (0.95), IMAT (0.91), and bone (0.92). Mean time to automatically segment one CT image was 0.07 s (GPU) and 2.51 s (CPU). Our CNN-based model enables accurate automated segmentation of multiple tissues on pelvic CT images, with promising implications for body composition studies.
To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segmentations as the reference standard. We manually segmented 200 axial CT images at the supra-acetabular level in 200 subjects, labeling background, subcutaneous adipose tissue (SAT), muscle, inter-muscular adipose tissue (IMAT), bone, and miscellaneous intra-pelvic content. The dataset was randomly divided into training (180/200) and test (20/200) datasets. Data augmentation was utilized to enlarge the training dataset and all images underwent preprocessing with histogram equalization. Our model was trained for 50 epochs using the U-Net architecture with batch size of 8, learning rate of 0.0001, Adadelta optimizer and a dropout of 0.20. The Dice (F1) score was used to assess similarity between the manual segmentations and the CNN predicted segmentations. The CNN model with data augmentation of N = 3000 achieved accurate segmentation of body composition for all classes. The Dice scores were as follows: background (1.00), miscellaneous intra-pelvic content (0.98), SAT (0.97), muscle (0.95), IMAT (0.91), and bone (0.92). Mean time to automatically segment one CT image was 0.07 s (GPU) and 2.51 s (CPU). Our CNN-based model enables accurate automated segmentation of multiple tissues on pelvic CT images, with promising implications for body composition studies.
Objective To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segmentations as the reference standard. Materials and methods We manually segmented 200 axial CT images at the supra-acetabular level in 200 subjects, labeling background, subcutaneous adipose tissue (SAT), muscle, inter-muscular adipose tissue (IMAT), bone, and miscellaneous intra-pelvic content. The dataset was randomly divided into training (180/200) and test (20/200) datasets. Data augmentation was utilized to enlarge the training dataset and all images underwent preprocessing with histogram equalization. Our model was trained for 50 epochs using the U-Net architecture with batch size of 8, learning rate of 0.0001, Adadelta optimizer and a dropout of 0.20. The Dice (F1) score was used to assess similarity between the manual segmentations and the CNN predicted segmentations. Results The CNN model with data augmentation of N  = 3000 achieved accurate segmentation of body composition for all classes. The Dice scores were as follows: background (1.00), miscellaneous intra-pelvic content (0.98), SAT (0.97), muscle (0.95), IMAT (0.91), and bone (0.92). Mean time to automatically segment one CT image was 0.07 s (GPU) and 2.51 s (CPU). Conclusions Our CNN-based model enables accurate automated segmentation of multiple tissues on pelvic CT images, with promising implications for body composition studies.
Audience Academic
Author Wang, Benjamin
Torriani, Martin
Hemke, Robert
Buckless, Colleen G.
Tsao, Andrew
AuthorAffiliation 1 Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
2 Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Academic Medical Center, Amsterdam Movement Sciences, Amsterdam, the Netherlands
AuthorAffiliation_xml – name: 1 Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
– name: 2 Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Academic Medical Center, Amsterdam Movement Sciences, Amsterdam, the Netherlands
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  givenname: Robert
  surname: Hemke
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  organization: Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Academic Medical Center, Amsterdam Movement Sciences
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  surname: Buckless
  fullname: Buckless, Colleen G.
  organization: Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School
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  givenname: Andrew
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/31396667$$D View this record in MEDLINE/PubMed
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ISSN 0364-2348
1432-2161
IngestDate Thu Aug 21 17:38:33 EDT 2025
Fri Jul 11 04:00:48 EDT 2025
Fri Jul 25 09:39:29 EDT 2025
Tue Jun 17 21:47:38 EDT 2025
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Mon Jul 21 06:01:20 EDT 2025
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Thu Apr 24 23:04:32 EDT 2025
Fri Feb 21 02:28:33 EST 2025
IsDoiOpenAccess false
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Issue 3
Keywords Deep learning
Muscle
Segmentation
Computed tomography
Pelvis
Body composition
Language English
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PublicationSubtitle Journal of the International Skeletal Society A Journal of Radiology, Pathology and Orthopedics
PublicationTitle Skeletal radiology
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  doi: 10.1093/gerona/60.3.324
– volume: 26
  start-page: 297
  year: 1945
  ident: 3289_CR23
  publication-title: Ecology.
  doi: 10.2307/1932409
SSID ssj0017756
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Snippet Objective To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We...
To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized...
Objective To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We...
ObjectiveTo develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We...
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StartPage 387
SubjectTerms Acetabulum
Adipose tissue
Adipose Tissue - diagnostic imaging
Adipose tissues
Artificial neural networks
Automation
Body Composition
Bone composition
Computed tomography
Contrast Media
CT imaging
Data augmentation
Datasets
Datasets as Topic
Deep learning
Equalization
Female
Histograms
Humans
Image processing
Image segmentation
Imaging
Male
Medical imaging equipment
Medicine
Medicine & Public Health
Middle Aged
Muscle, Skeletal - diagnostic imaging
Muscles
Neural networks
Neural Networks, Computer
Nuclear Medicine
Orthopedics
Pathology
Pelvis
Pelvis - diagnostic imaging
Physiological aspects
Radiology
Retrospective Studies
Scientific Article
Tomography, X-Ray Computed
Training
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  providerName: Springer Nature
Title Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment
URI https://link.springer.com/article/10.1007/s00256-019-03289-8
https://www.ncbi.nlm.nih.gov/pubmed/31396667
https://www.proquest.com/docview/2269782293
https://www.proquest.com/docview/2270010300
https://pubmed.ncbi.nlm.nih.gov/PMC6980503
Volume 49
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