Tissue segmentation: a crucial tool for quantitative MRI and visualization of anatomical structures

Schick, Fritz

doi:10.1007/s10334-016-0549-0

Cited by 16 publications

(10 citation statements)

References 36 publications

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“…Magnetic resonance imaging (MRI) is a suitable method for examining the effects of SCI and determining the effectiveness of subsequent rehabilitative interventions on skeletal muscle and adipose tissue distribution, because of its multiple forms of contrast weighting and its sensitivity to diffusion, perfusion, and chemical composition of tissues [12]. In particular, determining the SCI- and intervention-induced adaptations in functional key muscle groups, such as knee extensors and flexors, is important for understanding if appropriate muscle volume and ratio between key muscle groups are present at different stages post SCI, as this can affect motor function.…”

Section: Introductionmentioning

confidence: 99%

Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals

et al. 2019

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Severe spinal cord injury (SCI) leads to skeletal muscle atrophy and adipose tissue infiltration in the skeletal muscle, which can result in compromised muscle mechanical output and lead to health-related complications. In this study, we developed a novel automatic 3-D approach for volumetric segmentation and quantitative assessment of thigh Magnetic Resonance Imaging (MRI) volumes in individuals with chronic SCI as well as non-disabled individuals. In this framework, subcutaneous adipose tissue, inter-muscular adipose tissue and total muscle tissue are segmented using linear combination of discrete Gaussians algorithm. Also, three thigh muscle groups were segmented utilizing the proposed 3-D Joint Markov Gibbs Random Field model that integrates first order appearance model, spatial information, and shape model to localize the muscle groups. The accuracy of the automatic segmentation method was tested both on SCI (N = 16) and on non-disabled (N = 14) individuals, showing an overall 0.93±0.06 accuracy for adipose tissue and muscle compartments segmentation based on Dice Similarity Coefficient. The proposed framework for muscle compartment segmentation showed an overall higher accuracy compared to ANTs and STAPLE, two previously validated atlas-based segmentation methods. Also, the framework proposed in this study showed similar Dice accuracy and better Hausdorff distance measure to that obtained using DeepMedic Convolutional Neural Network structure, a well-known deep learning network for 3-D medical image segmentation. The automatic segmentation method proposed in this study can provide fast and accurate quantification of adipose and muscle tissues, which have important health and functional implications in the SCI population.

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Section: Introductionmentioning

confidence: 99%

Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals

et al. 2019

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“…They can generate a specific TI and TE to achieve reliable differentiation of all desired tissues. This is unlike combining several contrast weighting techniques which is usually insufficient to distinguish tissues [6]. When generated with the correct TIs or TEs sample points, synthetic weighted-images bring to light the subtle differences between tissues with close relaxation times, which help to increase classification accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…MR images are also affected by various signal artifacts like partial volume effects, transverse coherences or spoiling, B 0 - and B 1 -inhomogeneities [5], as well as hardware characteristics (e.g. intra- and inter-scanner variations, magnetic field intensity, coil sensitivity [6]). Furthermore, the tissue properties themselves have an impact on the acquired MR images (e.g.…”

Section: Introductionmentioning

confidence: 99%

Automatic classification of tissues on pelvic MRI based on relaxation times and support vector machine

Bojorquez¹,

Jodoin²,

Bricq³

et al. 2019

PLoS ONE

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Tissue segmentation and classification in MRI is a challenging task due to a lack of signal intensity standardization. MRI signal is dependent on the acquisition protocol, the coil profile, the scanner type, etc. While we can compute quantitative physical tissue properties independent of the hardware and the sequence parameters, it is still difficult to leverage these physical properties to segment and classify pelvic tissues. The proposed method integrates quantitative MRI values (T1 and T2 relaxation times and pure synthetic weighted images) and machine learning (Support Vector Machine (SVM)) to segment and classify tissues in the pelvic region, i.e.: fat, muscle, prostate, bone marrow, bladder, and air. Twenty-two men with a mean age of 30±14 years were included in this prospective study. The images were acquired with a 3 Tesla MRI scanner. An inversion recovery-prepared turbo spin echo sequence was used to obtain T1-weighted images at different inversion times with a TR of 14000 ms. A 32-echo spin echo sequence was used to obtain the T2-weighted images at different echo times with a TR of 5000 ms. T1 and T2 relaxation times, synthetic T1- and T2-weighted images and anatomical probabilistic maps were calculated and used as input features of a SVM for segmenting and classifying tissues within the pelvic region. The mean SVM classification accuracy across subjects was calculated for the different tissues: prostate (94.2%), fat (96.9%), muscle (95.8%), bone marrow (91%) and bladder (82.1%) indicating an excellent classification performance. However, the segmentation and classification for air (within the rectum) may not always be successful (mean SVM accuracy 47.5%) due to the lack of air data in the training and testing sets. Our findings suggest that SVM can reliably segment and classify tissues in the pelvic region.

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“…The tedious and rater-dependent nature of such manual thresholding techniques for paraspinal muscle composition assessment provides the impetus for the development of automated or semi-automated segmentation methods. Although, automated and sophisticated methods have been successfully implemented in MR image tissue segmentation of different anatomical structures including the brain, liver, heart [ 13 – 16 ], as well as the quantification of thigh muscle and adipose tissue [ 17 ], we are aware of only one recently developed semi-automated interactive tool for the assessment of paraspinal muscle composition [ 18 ]. The threshold values in the interactive segmentation technique, however, are based on visual inspection and, therefore, remain rater-dependent.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an automated thresholding algorithm for the quantification of paraspinal muscle composition from MRI images

et al. 2017

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BackgroundThe imaging assessment of paraspinal muscle morphology and fatty infiltration has gained considerable attention in the past decades, with reports suggesting an association between muscle degenerative changes and low back pain (LBP). To date, qualitative and quantitative approaches have been used to assess paraspinal muscle composition. Though highly reliable, manual thresholding techniques are time consuming and not always feasible in a clinical setting. The tedious and rater-dependent nature of such manual thresholding techniques provides the impetus for the development of automated or semi-automated segmentation methods. The purpose of the present study was to develop and evaluate an automated thresholding algorithm for the assessment of paraspinal muscle composition. The reliability and validity of the muscle measurements using the new automated thresholding algorithm were investigated through repeated measurements and comparison with measurements from an established, highly reliable manual thresholding technique.MethodsMagnetic resonance images of 30 patients with LBP were randomly selected cohort of patients participating in a project on commonly diagnosed lumbar pathologies in patients attending spine surgeon clinics. A series of T2-weighted MR images were used to train the algorithm; preprocessing techniques including adaptive histogram equalization method image adjustment scheme were used to enhance the quality and contrast of the images. All muscle measurements were repeated twice using a manual thresholding technique and the novel automated thresholding algorithm, from axial T2-weigthed images, at least 5 days apart. The rater was blinded to all earlier measurements. Inter-method agreement and intra-rater reliability for each measurement method were assessed. The study did not received external funding and the authors have no disclosures.ResultsThere was excellent agreement between the two methods with inter-method reliability coefficients (intraclass correlation coefficients) varying from 0.79 to 0.99. Bland and Altman plots further confirmed the agreement between the two methods. Intra-rater reliability and standard error of measurements were comparable between methods, with reliability coefficient varying between 0.95 and 0.99 for the manual thresholding and 0.97–0.99 for the automated algorithm.ConclusionThe proposed automated thresholding algorithm to assess paraspinal muscle size and composition measurements was highly reliable, with excellent agreement with the reference manual thresholding method.

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Tissue segmentation: a crucial tool for quantitative MRI and visualization of anatomical structures

Cited by 16 publications

References 36 publications

Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals

Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals

Automatic classification of tissues on pelvic MRI based on relaxation times and support vector machine

Evaluation of an automated thresholding algorithm for the quantification of paraspinal muscle composition from MRI images

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