Rheumatoid arthritis (RA) is a disease characterized by progressive and irreversible destruction of bones and joints. According to current recommendations, magnetic resonance imaging (MRI) is used to asses three main ...
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Rheumatoid arthritis (RA) is a disease characterized by progressive and irreversible destruction of bones and joints. According to current recommendations, magnetic resonance imaging (MRI) is used to asses three main signs of RA based on manual evaluation of MR images: synovitis, bone edema and bone erosions. The key feature of a future computer-assisted diagnostic system for evaluation RA lesions is accurate segmentation of 15 wrist bones. In the present paper, we focus on developing a wrist bones segmentation framework. The segmentation procedure consisted of three stages: segmentation of the distal parts of ulna and radius, segmentation of the proximal parts of metacarpal bones and segmentation of carpal bones. At every stage, markers of bones were determined first, using an atlas-based approach. Then, given markers of bones and a marker of background, a watershed from markers algorithm was applied to find the final segmentation. The MR data for 37 cases were analyzed. The automated segmentation results were compared with gold-standard manual segmentations using a few well-established metrics: area under ROC curve AUC, mean similarity MS and mean absolute distance MAD. The mean (standard deviation) values of AUC, MS and MAD were 0.97 (0.04), 0.93 (0.09) and 1.23 (0.28), respectively. The results of the present study demonstrate that automated segmentation of wrist bones is feasible. The proposed algorithm can be the first stage for the detection of early lesions like bone edema or synovitis.
Paraspinal muscles support spine and are the source of movement force. The cross section area (CSA) size, shape, density and volume are affected by many factors, such as surgery, age, health condition, exercise, and l...
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ISBN:
(纸本)9781467390880
Paraspinal muscles support spine and are the source of movement force. The cross section area (CSA) size, shape, density and volume are affected by many factors, such as surgery, age, health condition, exercise, and low back pain. Minimally invasive spine surgery (MISS) was introduced to provide less muscle tissue injury, less postoperative pain and earlier mobilization than traditional open back surgery. Manual measurements of paraspinal muscle CSA and volume in CT images is inaccurate and time consuming. In this work, an atlas-basedimage registration is used to segment the muscle region in CT images. In order to address the challenge of large variations of muscle shape and region direction, a local contour optimization is performed after global registration. Experimental results show that the proposed method can successfully segment paraspinal muscle regions in target images. The results can be used to evaluate paraspinal muscle volume hence tissue injury and postoperative back muscle atrophy of MISS patients.
Paraspinal muscles support the spine and are the source of movement force. The size, shape, density, and volume of the paraspinal muscles cross-section area(CSA) are affected by many factors, such as age, health condi...
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ISBN:
(纸本)9781538619797;9781538619780
Paraspinal muscles support the spine and are the source of movement force. The size, shape, density, and volume of the paraspinal muscles cross-section area(CSA) are affected by many factors, such as age, health condition, exercise, and low back pain. It is invaluable to segment the paraspinal muscle regions in images in order to measure and study them. Manual segmentation of the paraspinal muscle CSA is time-consuming and inaccurate. In this work, an atlas-based image segmentation algorithm is proposed to segment the paraspinal muscles in CT images. To address the challenges of variations of muscle shape and its relative spatial relationship to other organs, mutual information is utilized to register the atlas and target images, followed by gradient vector flow contour deformation. Experimental results show that the proposed method can successfully segment paraspinal muscle regions in CT images in both intrapatient and interpatient cases. Furthermore, using mutual information to register atlas and target images outperforms the method using spine-spine registration. It segments the muscle regions accurately without the need of the computationally expensive iterative local contour optimization. The results can be used to evaluate paraspinal muscle tissue injury and postoperative back muscle atrophy of spine surgery patients.
atlases are frequently employed to assist medical imagesegmentation with prior information. This paper introduces a multi-atlas architecture that is trained to locally characterize the appearance (average intensity a...
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ISBN:
(纸本)9781728131498
atlases are frequently employed to assist medical imagesegmentation with prior information. This paper introduces a multi-atlas architecture that is trained to locally characterize the appearance (average intensity and standard deviation) of normal tissues in various observed and computed data channels of brain MRI records. The multiple atlas is then deployed to enhance the accuracy of an ensemble learning based brain tumor segmentation procedure that uses binary decision trees. The proposed method is validated using the low-grade tumor volumes of the BraTS 2016 train data set. The use of atlases improve the segmentation quality, causing a rise of up to 1.5% in average Dices scores.
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