KEYWORDS: Image segmentation, Muscles, Magnetic resonance imaging, Education and training, Deep learning, 3D modeling, Deformation, 3D image processing, Tissues, Anatomy
PurposeSegmentation is essential for tissue quantification and characterization in studies of aging and age-related and metabolic diseases and the development of imaging biomarkers. We propose a multi-method and multi-atlas methodology for automated segmentation of functional muscle groups in three-dimensional (3D) thigh magnetic resonance images. These groups lie anatomically adjacent to each other, rendering their manual delineation a challenging and time-consuming task.ApproachWe introduce a framework for automated segmentation of the four main functional muscle groups of the thigh, gracilis, hamstring, quadriceps femoris, and sartorius, using chemical shift encoded water–fat magnetic resonance imaging (CSE-MRI). We propose fusing anatomical mappings from multiple deformable models with 3D deep learning model–based segmentation. This approach leverages the generalizability of multi-atlas segmentation (MAS) and accuracy of deep networks, hence enabling accurate assessment of volume and fat content of muscle groups.ResultsFor segmentation performance evaluation, we calculated the Dice similarity coefficient (DSC) and Hausdorff distance 95th percentile (HD-95). We evaluated the proposed framework, its variants, and baseline methods on 15 healthy subjects by threefold cross-validation and tested on four patients. Fusion of multiple atlases, deformable registration models, and deep learning segmentation produced the top performance with an average DSC of 0.859 and HD-95 of 8.34 over all muscles.ConclusionsFusion of multiple anatomical mappings from multiple MAS techniques enriches the template set and improves the segmentation accuracy. Additional fusion with deep network decisions applied to the subject space offers complementary information. The proposed approach can produce accurate segmentation of individual muscle groups in 3D thigh MRI scans.
Segmentation is an essential tool for quantification and characterization of tissue properties, with applications ranging from assessment of body composition, disease diagnosis, to development of imaging biomarkers. In this work, we propose a multi-method and multi-atlas methodology for automated segmentation of functional muscle groups in 3D Dixon MR images of the mid-thigh. The functional muscle groups addressed in this paper lie anatomically close to each other, that makes segmentation an arduous task for accuracy. We propose an approach that uses anatomical mappings enabling delineation of adjacent muscle groups that are difficult to separate using conventional intensity-based patterns only. We segment the four functional muscle groups of the thigh in both legs by multi-atlas anatomical mappings and fuse the labels to improve delineation accuracy. We investigate the fusion of segmentation from multiple atlases and multiple deformable registration methods. For performance evaluation we applied cross-validation by excluding the scans that served as templates in our framework and report DSC values on the remaining test scans. We evaluated four individual deformable models, free-form deformation (FFD), symmetric normalization (SYN), symmetric diffeomorphic demons (SDD), and Voxelmorph (VXM), and the joint multi-method fusion. Multi-atlas and multi-method fusion produced the top average DSC of 0.795 over all muscles on the test scans.
KEYWORDS: Muscles, Image segmentation, Magnetic resonance imaging, Deformation, Silver, Image registration, Tissues, 3D image processing, 3D magnetic resonance imaging, Matrices
We introduce a multi-atlas-based image segmentation (MAIS) framework for the four functional muscle groups, gracilis, hamstring, quadriceps femoris, and sartorius, of the left and right thighs, using 3D chemical shift encoding-based MRI scans obtained from the MyoSegmenTUM database. We generated a statistical atlas and its silver truth by statistical approaches and employed block-matching and 3D filtering (BM3D) to deblur the statistical atlas. We segmented the four pairs of the functional muscle groups of the thigh using three templates, including the statistical atlas, and fused the labels using STAPLE. We validated the performance of our method by calculating the Dice similarity coefficient (DSC) between the delineated muscle group and its ground truth. We also compared the performance of four deformable models: free-form deformation (FFD), two versions of symmetric normalization (SYN and SYNO), and symmetric diffeomorphic demons (SDD). Our results show that SDD with STAPLE produced a mean DSC of 0.784 over all muscle groups. These results imply that the proposed technique has great potential for quantification and characterization of individual muscle groups.
Quantification of the strength and quality of the muscle, adipose tissue, and bone is important for the characterization of the effects of normal aging, age-related diseases, metabolic disorders and neuromuscular diseases. The cost, duration and risks of medical imaging trials may render the task of generating a sufficient high number of quality data for the training of systems for clinical trials and evaluations impracticable. In this work, we developed a model of the human mid-thigh with structural representation of its muscles, adipose tissue and bone anatomy, for use in virtual clinical studies. This is a simulation-based approach to optimizing medical imaging systems. We simulated thigh phantoms based on the OpenVCT software framework, originally designed for digital breast imaging studies. We designed and generated phantoms of mid-thigh anatomical structures representing normal anatomy. Exploiting the flexibility of the system, we were able to generate a controlled population for which we varied, separately, different anatomical structures. We simulated regional mid-thigh muscle area degeneration that is frequently observed in diabetes patients and quantified the structural changes relatively to a healthy anatomy. This framework also allows us to simulate variations of anatomical structures – hence serving as a system for advanced data augmentation that may be used for training machine learning-based diagnostic methods, simulating the effects of diseases, and designing clinical studies.
Accurate and reproducible tissue identification techniques are essential for understanding structural and functional changes that either occur naturally with aging, or because of chronic disease, or in response to intervention therapies. These image analysis techniques are frequently utilized for characterization of changes in bone architecture to assess fracture risk, and for the assessment of loss of muscle mass and strength defined as sarcopenia. Peripheral quantitative computed tomography (pQCT) is widely employed for tissue identification and analysis. Advantages of pQCT scanners are compactness, portability, and low radiation dose. However, these characteristics imply limitations in spatial resolution and SNR. Therefore, there is still a need for segmentation methods that address image quality limitations and artifacts such as patient motion. In this paper, we introduce multi-atlas segmentation (MAS) techniques to identify soft and hard tissues in pQCT scans of the proximal tibia (~ 66% of tibial length) and to address the above factors that limit delineation accuracy. To calculate the deformation fields, we employed multi-grid free-form deformation (FFD) models with B-splines and a symmetric extension of the log-domain diffeomorphic demons (SDD). We then applied majority voting and Simultaneous Truth And Performance Level Estimation (STAPLE) for label fusion. We compared the results of our MAS methodology for each deformable registration model and each label fusion method, using Dice similarity coefficient scores (DSC). The results show that our technique utilizing SDD with STAPLE produces very good accuracy (DSC mean of 0.868) over all tissues, even for scans with considerable quality degradations caused by motion artifacts.
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