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Kafali SG, Shih SF, Li X, Kim GHJ, Kelly T, Chowdhury S, Loong S, Moretz J, Barnes SR, Li Z, Wu HH. Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-023-01146-3. [PMID: 38300360 DOI: 10.1007/s10334-023-01146-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 12/18/2023] [Accepted: 12/27/2023] [Indexed: 02/02/2024]
Abstract
OBJECTIVE Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with overweight/obesity using attention-based competitive dense (ACD) 3D U-Net and 3D nnU-Net with full field-of-view volumetric multi-contrast inputs. MATERIALS AND METHODS 920 adults with overweight/obesity were scanned twice at multiple 3 T MRI scanners and institutions. The first scan was divided into training/validation/testing sets (n = 646/92/182). The second scan from the subjects in the testing set was used to evaluate the generalizability for longitudinal analysis. Segmentation performance was assessed by measuring Dice scores (DICE-SAT, DICE-VAT), false negatives (FN), and false positives (FP). Volume agreement was assessed using the intraclass correlation coefficient (ICC). RESULTS ACD 3D U-Net achieved rapid (< 4.8 s/subject) segmentation with high DICE-SAT (median ≥ 0.994) and DICE-VAT (median ≥ 0.976), small FN (median ≤ 0.7%), and FP (median ≤ 1.1%). 3D nnU-Net yielded rapid (< 2.5 s/subject) segmentation with similar DICE-SAT (median ≥ 0.992), DICE-VAT (median ≥ 0.979), FN (median ≤ 1.1%) and FP (median ≤ 1.2%). Both models yielded excellent agreement in SAT/VAT volume versus reference measurements (ICC > 0.997) in longitudinal analysis. DISCUSSION ACD 3D U-Net and 3D nnU-Net can be automated tools to quantify abdominal SAT/VAT volume rapidly, accurately, and longitudinally in adults with overweight/obesity.
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Affiliation(s)
- Sevgi Gokce Kafali
- Department of Radiological Sciences, University of California, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA
- Department of Bioengineering, University of California, Los Angeles, CA, USA
| | - Shu-Fu Shih
- Department of Radiological Sciences, University of California, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA
- Department of Bioengineering, University of California, Los Angeles, CA, USA
| | - Xinzhou Li
- Department of Radiological Sciences, University of California, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA
| | - Grace Hyun J Kim
- Department of Radiological Sciences, University of California, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA
| | - Tristan Kelly
- Department of Physiological Science, University of California, Los Angeles, CA, USA
| | - Shilpy Chowdhury
- Department of Radiology, Loma Linda University Medical Center, Loma Linda, CA, USA
| | - Spencer Loong
- Department of Psychology, Loma Linda University School of Behavioral Health, Loma Linda, CA, USA
| | - Jeremy Moretz
- Department of Neuroradiology, Loma Linda University Medical Center, Loma Linda, CA, USA
| | - Samuel R Barnes
- Department of Radiology, Loma Linda University Medical Center, Loma Linda, CA, USA
| | - Zhaoping Li
- Department of Medicine, University of California, Los Angeles, CA, USA
| | - Holden H Wu
- Department of Radiological Sciences, University of California, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA.
- Department of Bioengineering, University of California, Los Angeles, CA, USA.
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