Simultaneous Volumetric Segmentation of Vertebral Bodies and Intervertebral Discs on Fat-Water MR Images

Fat-water magnetic resonance (MR) images allow automated noninvasive analysis of morphological properties and fat fractions of vertebral bodies (VBs) and inter-vertebral discs (IVDs) that constitute an important part of human biomechanical systems. In this paper, we propose a fully automated approac...

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Veröffentlicht in:IEEE journal of biomedical and health informatics 2019-07, Vol.23 (4), p.1692-1701
Hauptverfasser: Fallah, Faezeh, Walter, Sven Stephan, Bamberg, Fabian, Yang, Bin
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container_issue 4
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container_title IEEE journal of biomedical and health informatics
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creator Fallah, Faezeh
Walter, Sven Stephan
Bamberg, Fabian
Yang, Bin
description Fat-water magnetic resonance (MR) images allow automated noninvasive analysis of morphological properties and fat fractions of vertebral bodies (VBs) and inter-vertebral discs (IVDs) that constitute an important part of human biomechanical systems. In this paper, we propose a fully automated approach for simultaneously segmenting multiple VBs and IVDs on fat-water MR images without prior localization or geometry estimation. This method involved a hierarchical random forest (HRF) classifier and a hierarchical conditional random field (HCRF) that encoded a multiresolution image pyramid based on a set of multiscale local and contextual features. The HRF classifier employed penalized multivariate linear discriminants and SMOTE Bagging to handle limited and imbalanced training data with large feature dimension. The HCRF estimated optimum labels according to their spatial and hierarchical consistencies by using the layer-wise significant features determined over the trained HRF classifier. To handle variable sample numbers at different resolutions, resolution-specific hyper-parameters were used. This method was trained and evaluated for segmenting 15 thoracic and lumbar VBs and their IVDs on fat-water MR images of a subset of a large cohort data set. It was further evaluated for segmenting seven IVDs of the lower spine on fat-water images of a public grand challenge. These evaluations revealed the comparable accuracy of this method with the state-of-the-art while requiring less computational burden due to a simultaneous localization and segmentation.
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subjects Adipose Tissue - diagnostic imaging
Adult
Algorithms
Automation
Biomechanics
Body Water - diagnostic imaging
Classifiers
Coding
Computer applications
Conditional random fields
Fats
Feature extraction
Female
Hierarchical conditional random field
hierarchical random forest
Humans
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Intervertebral Disc - diagnostic imaging
Intervertebral discs
Localization
Lumbar Vertebrae - diagnostic imaging
Magnetic properties
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Middle Aged
penalized multivariate linear discriminant
Spatial resolution
Spine
Spine (lumbar)
Thorax
Training
Training data
Vertebrae
vertebral bodies
title Simultaneous Volumetric Segmentation of Vertebral Bodies and Intervertebral Discs on Fat-Water MR Images
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