Post on 24-Jul-2020
Automated abdominal segmentation of CT scans for body composition analysis using U-Net
Alexander Weston, Panagiotis Korfiatis PhD, Timothy Kline PhD, Kenneth Philbrick PhD, Petro Kostandy PhD, Motokazu Sugimoto
MD, Naoki Takahashi MD, Bradley Erickson MD, PhD
Radiology Informatics Lab, Mayo Clinic
CMIMI ConferenceSeptember 9th 2018
Body CompositionAmount and distribution of fat and muscle
Linked to outcomes in a number of conditions
diabetesheart diseasecancerkidney diseasesurgical outcomesquality of life
Topic of research –obesity paradoxmetabolic syndromevisceral vs subcutaneous obesity
Woman with Mirror, Francisco Botero
Measuring Body Composition
Several clinical techniquesBody mass indexSkin-fold testGrip strengthAir-displacement plethysmographyBioelectric Impedance AnalysisHydrostatic WeighingDual-energy X-ray absorptiometry
CT – most available?
Limitations: Segmentation
Dataset
Parameter N = 1429
Male, number (%) 878 (61)
Age range, min - max 29 – 97
Average age, mean (SD) 66 (11.3)
Scan date range 1997–2015
Parameter N (%) or mean ± SD
mA 362 ± 148
kVp
100 171 (6)
120 2384 (88)
130 53 (2)
140 89 (3)
Other 10 (1)
Slice Thickness
<2mm 37 (1)
2 - 3mm 1105 (41)
3 - 4mm 277 (10)
5mm 1152 (43)
>5mm 136 (5)
Patient parameters Acquisition parameters
ModelModel
Layers 12
Activation Function
Hyperbolic tangent (tanh)
Output activation
Softmax
Hyper-parametersLearning rate 0.0005
Optimizer Adam
Loss Categorical Crossentropy
Batch Size 16
TrainingEpochs: (early stopping) 196
Software Keras with TF backend
Hardware Nvidia Tesla V100 GPU (16GB RAM)
Results on the test setCompartment Prediction vs
gold-standardInter-rater Prediction vs
gold-standard
Dice score,mean (SD)
Dice score, mean (SD)
Area difference, mean (SD)
Subcutaneous adipose tissue
0.98 (0.01) 0.95 (0.02) 1.1% (2.0%)
Muscle 0.96 (0.02) 0.93 (0.02) 2.1% (2.5%)
Visceral adipose tissue 0.94 (0.12) N/A 1.4% (1.8%)
Visceral organ tissue 0.97 (0.01) N/A 3.7% (12.2%)
Bone 0.98 (0.02) 0.95 (0.02) 4.3% (3.6%)
Visual results on the test set
Prediction Gold-standard
853 cm2 845 cm2
177 cm2 171 cm2
418 cm2 415 cm2
Prediction Gold-standard
103 cm2 104 cm2
186 cm2 185 cm2
89 cm2 89 cm2
Scan Gold-standardPredictionObese
Healthy-weight
Subcutaneous adipose tissue
Muscle
Visceral adipose tissue
Visceral organ tissue
Results on a secondary datasetScan
Gold-standardPrediction
Compartment Prediction vs gold-standard, liver
Dice score, mean (SD) Area diff, mean (SD)
SubQ adipose tissue 0.94 (0.05) 6.6% (8.1%)
Muscle 0.92 (0.04) 4.3% (4.8%)
Viscera (combined) 0.99 (0.01) 0.9% (1.0%)
3D segmentation
Annotation by iterative deep-learning (AID)Original Update 1 Update 2
Conclusions and implicationsGenerate accurate metrics related to patient health in seconds, not hours
Increasingly accurate algorithm through iterative retraining
In research –Faster analysisMore accurate data2D vs 3D
In clinical practiceRuns on existing, unused clinical dataHigh quality, novel metrics of patient health
Thank you,Radiology Informatics Lab
Bradley Erickson, MD, PhD
Naoki Takahashi, MD
Arunnit Boonrod, MD
Panagiotis Korfiatis, PhD
Bill RyanPetro Kostandy, MD
Zeynettin Akkus, PhD
Timothy Kline, PhD
Issa Ali
Kenneth Philbrick, PhD
Motokazu Sugimoto, MD
Scott SquiresAtefeh Zeinoddini, MD
Gian Marco Conte, MD
Supplemental Slides
Metrics
BADice = ( 2 * 2 ) / ( 3 + 4 ) = 0.57
TPF = ( 2 ) / ( 3 ) = 0.67
FPF = ( ( 3 + 4 – 2) – 3 ) / ( 3 ) = 0.67|A| = 3|B| = 4|A∩B| = 2
A∩B2 ∙ | A ∩ B || A + B |
Dice =
Metrics
Dice score
Jaccard coefficient
True Positive Fraction (TPF)
False Positive Fraction (FPF)
𝐴𝐴 ∪ 𝐵𝐵𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐵𝐵
2 ∙ | A ∩ B || A + B |
| A ∩ B || A U B |
| A ∩ B || A |
| (B U A) – A || A |
Biomarkers of body composition
Image from Mayo Clinic, https://www.mayoclinic.org/diseases-conditions/metabolic-syndrome/multimedia/apple-and-pear-body-shapes/img-20006114
visceral adiposity
intramuscular adipose deposits
lean muscle
subcutaneous adiposity
Dual-Energy X-Ray Absorptiometry (DXA)
Image from Mayo Clinic, https://mayoclinichealthsystem.org/locations/springfield/services-and-treatments/radiology-and-imaging/dexa-scan
Image from Queensland X-Ray, https://www.qldxray.com.au/our-services/body-composition-scanning/
Low energy whole-body X-Ray Developed for osteoporosis,
can also measure adiposity Additional scan
Results against gold-standard
Compare to: Lee, H., Troschel, F. M., Tajmir, S., Fuchs, G., Mario, J., Fintelmann, F. J., & Do, S. (2017). Pixel-level deep segmentation: artificial intelligence quantifies muscle on computed tomography for body morphometric analysis. Journal of digital imaging, 30(4), 487-498.
Results against expert physician
Results on HCC Dataset
Bland-Altman plots
Performance on L4 vertebra of model trained on L3