Obstructive sleep apnea syndrome (OSAS) is one of the most common sleep disorders that endangers human health, which is associated with episodes of apnea or hypopnea during sleep. In children, OSAS is associated with cardiovascular morbidity, neurobehavioral deficits, and poor quality of life, which highlights the importance for early diagnosis and treatment. Recent studies using dynamic magnetic resonance imaging (dMRI) have shown that adults with OSAS exhibit airway narrowing in specific regions that display increased variability in diameter during sleep as compared with controls. In this paper, we propose a novel method to compare OSAS patients with control subjects during awake and asleep states to assess the regional dynamic changes that occur in specific locations of the upper airway. Firstly, we segment the 3D upper airway with a previously developed fully automatic method. Then, different types of breathing cycles are selected by experts based on polysomnography. For each cycle, we calculate the distance of each point on the surface of the upper airway from end-expiration (EE) to end-inspiration (EI), which is then utilized for subsequent motion analysis. The 3D upper airway is subsequently divided into 4 anatomical parts manually. Lastly, comparisons of the dynamic upper airway motion measurements from different cycle groups are performed between OSAS patients and control subjects. Comparisons of different types of cycles within the same anatomical part demonstrated significant differences between control subjects and OSAS patients in all anatomic parts with some exceptions. These novel observations may provide some insights into the pathophysiology of OSAS.
Currently, 3T hip MRI can be used to estimate femur strength and cortical bone thickness. One of the major hurdles in this application is that objects (osseous structures) are manually segmented which involves significant human labor. In this study, we propose an automatic and accurate algorithm for osseous structure segmentation from hip 3T MRI by using a deep convolutional neural network. The approach includes two stages: 1) automatic localization of acetabulum and femur by using the femoral head as a reference, and 2) 2D bounding box (BB) set up for each object based on the localization information from femoral head followed by a UNet to segment the target object within the BB. 90 3T hip MRI image data sets were utilized in this study that were divided into training, validating, and testing groups (60%:20%:20%), and a 5-fold cross-validation was adopted in the procedure. The study showed that automated segmentation results were comparable to the reference standard from manual segmentation. The average Dice Coefficient for acetabular and femoral (i.e., cortical and medullary bone plus bone marrow) segmentation was 0.93 and 0.96, respectively. Segmentations of acetabular and femoral medullary cavity (i.e., medullary bone plus bone marrow) had Dice Coefficient of 0.89 and 0.95, respectively. Acetabular and femoral cortical bone segmentations were more challenging with lower Dice Coefficient of around 0.7. The proposed approach is automatic and effective without any interaction from humans. The idea of using local salient anatomy to guide object localization approaches is heuristic and can be easily generalized to other localization problems in practice.
KEYWORDS: Magnetic resonance imaging, Convolutional neural networks, Image segmentation, 3D magnetic resonance imaging, Visualization, Tissues, Quantitative analysis, 3D modeling
Upper airway segmentation in static and dynamic MRI is a prerequisite step for quantitative analysis in patients with disorders such as obstructive sleep apnea. Recently, some semi-automatic methods have been proposed with high segmentation accuracy. However, the low efficiency of such methods makes it difficult to implement for the processing of large numbers of MRI datasets. Therefore, a fully automatic upper airway segmentation approach is needed. In this paper, we present a novel automatic upper airway segmentation approach based on convolutional neural networks. Firstly, we utilize the U-Net network as the basic model for learning the multi-scale feature from adjacent image slices and predicting the pixel-wise label in MRI. In particular, we train three networks with the same structure for segmenting the pharynx/larynx and nasal cavity separately in axial static 3D MRI and axial dynamic 2D MRI. The visualization and quantitative results demonstrate that our approach can be applied to various MRI acquisition protocols with high accuracy and stability.
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