Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalization of a Musculoskeletal Model

by   Yuta Hiasa, et al.

We propose a method for automatic segmentation of individual muscles from a clinical CT. The method uses Bayesian convolutional neural networks with the U-Net architecture, using Monte Carlo dropout that infers an uncertainty metric in addition to the segmentation label. We evaluated the performance of the proposed method using two data sets: 20 fully annotated CTs of the hip and thigh regions and 18 partially annotated CTs that are publicly available from The Cancer Imaging Archive (TCIA) database. The experiments showed a Dice coefficient (DC) of 0.891 +/- 0.016 (mean +/- std) and an average symmetric surface distance (ASD) of 0.994 +/- 0.230 mm over 19 muscles in the set of 20 CTs. These results were statistically significant improvements compared to the state-of-the-art hierarchical multi-atlas method which resulted in 0.845 +/- 0.031 DC and 1.556 +/- 0.444 mm ASD. We evaluated validity of the uncertainty metric in the multi-class organ segmentation problem and demonstrated a correlation between the pixels with high uncertainty and the segmentation failure. One application of the uncertainty metric in active-learning is demonstrated, and the proposed query pixel selection method considerably reduced the manual annotation cost for expanding the training data set. The proposed method allows an accurate patient-specific analysis of individual muscle shapes in a clinical routine. This would open up various applications including personalization of biomechanical simulation and quantitative evaluation of muscle atrophy.


page 2

page 5

page 6

page 8

page 9

page 12

page 13

page 14


Robust Segmentation Models using an Uncertainty Slice Sampling Based Annotation Workflow

Semantic segmentation neural networks require pixel-level annotations in...

Multi-Atlas Segmentation with Joint Label Fusion of Osteoporotic Vertebral Compression Fractures on CT

The precise and accurate segmentation of the vertebral column is essenti...

Microscopic Nuclei Classification, Segmentation and Detection with improved Deep Convolutional Neural Network (DCNN) Approaches

Due to cellular heterogeneity, cell nuclei classification, segmentation,...

Bayesian convolutional neural network based MRI brain extraction on nonhuman primates

Brain extraction or skull stripping of magnetic resonance images (MRI) i...

Improving Vertebra Segmentation through Joint Vertebra-Rib Atlases

Accurate spine segmentation allows for improved identification and quant...

Boosting Segmentation Performance across datasets using histogram specification with application to pelvic bone segmentation

Accurate segmentation of the pelvic CTs is crucial for the clinical diag...

Please sign up or login with your details

Forgot password? Click here to reset