The Relationship Between Symptoms and Abnormal Magnetic Resonance Images of Lumbar Intervertebral Disks

The purpose of this study is to critically investigate the anatomy of the deep posterior compartment of the leg. Specifically, the relationship of the deep posterior compartment to the superficial posterior compartment and their insertion onto the posteromedial border of the tibia are assessed. Cross-sectioning of 10 fresh-frozen cadaver legs was performed at 2-cm increments. The inferior surface of each section was photographed. The photographs were visually analyzed, and the fascial separation between the posterior compartments along with their relationship to the posteromedial border of the tibia were recorded for each specimen. Magnetic resonance images in the axial plane of 10 healthy, normal volunteers’ lower extremities at 2-cm increments were obtained and analyzed. All specimens and images demonstrated that the medial fascial attachment of the deep posterior compartment was along the posteromedial aspect of the tibia in the proximal third of the leg and was not superficially accessible. In the proximal third of the leg, the superficial posterior compartment fascial attachment overlapped the deep posterior compartment by inserting medial and anterior to the deep posterior compartment fascial attachment. In the middle and distal thirds of the leg, the medial fascial attachment of the deep posterior compartment shifted medially and anteriorly, making the deep posterior compartment superficially accessible. The surgeon must appreciate the change in the anatomic relationships along the medial side of the leg while performing double-incision four-compartment fasciotomy release to obtain a complete release of the muscular portion of the deep posterior compartment.

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Atlas-Free Cervical Spinal Cord Segmentation on Midsagittal T2-Weighted Magnetic Resonance Images

Journal of Healthcare Engineering ◽

10.1155/2017/8691505 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Chun-Chih Liao ◽

Hsien-Wei Ting ◽

Furen Xiao

Keyword(s):

Spinal Cord ◽

Magnetic Resonance ◽

Expectation Maximization ◽

Cervical Spinal Cord ◽

Region Growing ◽

Magnetic Resonance Images ◽

Spinal Diseases ◽

Intervertebral Disks ◽

Vertebral Bodies ◽

Aided Diagnosis

An automatic atlas-free method for segmenting the cervical spinal cord on midsagittal T2-weighted magnetic resonance images (MRI) is presented. Pertinent anatomical knowledge is transformed into constraints employed at different stages of the algorithm. After picking up the midsagittal image, the spinal cord is detected using expectation maximization and dynamic programming (DP). Using DP, the anterior and posterior edges of the spinal canal and the vertebral column are detected. The vertebral bodies and the intervertebral disks are then segmented using region growing. Then, the anterior and posterior edges of the spinal cord are detected using median filtering followed by DP. We applied this method to 79 noncontrast MRI studies over a 3-month period. The spinal cords were detected in all cases, and the vertebral bodies were successfully labeled in 67 (85%) of them. Our algorithm had very good performance. Compared to manual segmentation results, the Jaccard indices ranged from 0.937 to 1, with a mean of 0.980 ± 0.014. The Hausdorff distances between the automatically detected and manually delineated anterior and posterior spinal cord edges were both 1.0 ± 0.5 mm. Used alone or in combination, our method lays a foundation for computer-aided diagnosis of spinal diseases, particularly cervical spondylotic myelopathy.

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Knee Cartilage Thickness Differs Alongside Ages: A 3-T Magnetic Resonance Research Upon 2,481 Subjects via Deep Learning

Frontiers in Medicine ◽

10.3389/fmed.2020.600049 ◽

2021 ◽

Vol 7 ◽

Author(s):

Liping Si ◽

Kai Xuan ◽

Jingyu Zhong ◽

Jiayu Huo ◽

Yue Xing ◽

...

Keyword(s):

Regression Analysis ◽

Deep Learning ◽

Magnetic Resonance ◽

Knee Joint ◽

Magnetic Resonance Images ◽

Cartilage Thickness ◽

Imaging Data ◽

Knee Cartilage ◽

Large Collection ◽

The Relationship

Background: It was difficult to distinguish the cartilage thinning of an entire knee joint and to track the evolution of cartilage morphology alongside ages in the general population, which was of great significance for studying osteoarthritis until big imaging data and artificial intelligence are fused. The purposes of our study are (1) to explore the cartilage thickness in anatomical regions of the knee joint among a large collection of healthy knees, and (2) to investigate the relationship between the thinning pattern of the cartilages and the increasing ages.Methods: In this retrospective study, 2,481 healthy knees (subjects ranging from 15 to 64 years old, mean age: 35 ± 10 years) were recruited. With magnetic resonance images of knees acquired on a 3-T superconducting scanner, we automatically and precisely segmented the cartilage via deep learning and calculated the cartilage thickness in 14 anatomical regions. The thickness readings were compared using ANOVA by considering the factors of age, sex, and side. We further tracked the relationship between the thinning pattern of the cartilage thickness and the increasing ages by regression analysis.Results: The cartilage thickness was always thicker in the femur than corresponding regions in the tibia (p < 0.05). Regression analysis suggested cartilage thinning alongside ages in all regions (p < 0.05) except for medial and lateral anterior tibia in both females and males (p > 0.05). The thinning speed of men was faster than women in medial anterior and lateral anterior femur, yet slower in the medial patella (p < 0.05).Conclusion: We established the calculation method of cartilage thickness using big data and deep learning. We demonstrated that cartilage thickness differed across individual regions in the knee joint. Cartilage thinning alongside ages was identified, and the thinning pattern was consistent in the tibia while inconsistent in patellar and femoral between sexes. These findings provide a potential reference to detect cartilage anomaly.

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