Quantification of fat in the posterior sacroiliac joint region applying a semi-automated segmentation method

2020 ◽  
Vol 191 ◽  
pp. 105386 ◽  
Author(s):  
Amélie Poilliot ◽  
Murray Tannock ◽  
Ming Zhang ◽  
Johann Zwirner ◽  
Niels Hammer
2021 ◽  
Vol 10 (2) ◽  
pp. 205846012098809
Author(s):  
Byeong H Oh ◽  
Hyeong C Moon ◽  
Aryun Kim ◽  
Hyeon J Kim ◽  
Chae J Cheong ◽  
...  

Background The pathology of Parkinson’s disease leads to morphological changes in brain structure. Currently, the progressive changes in gray matter volume that occur with time and are specific to patients with Parkinson’s disease, compared to healthy controls, remain unclear. High-tesla magnetic resonance imaging might be useful in differentiating neurological disorders by brain cortical changes. Purpose We aimed to investigate patterns in gray matter changes in patients with Parkinson’s disease by using an automated segmentation method with 7-tesla magnetic resonance imaging. Material and Methods High-resolution T1-weighted 7 tesla magnetic resonance imaging volumes of 24 hemispheres were acquired from 12 Parkinson’s disease patients and 12 age- and sex-matched healthy controls with median ages of 64.5 (range, 41–82) years and 60.5 (range, 25–74) years, respectively. Subgroup analysis was performed according to whether axial motor symptoms were present in the Parkinson’s disease patients. Cortical volume, cortical thickness, and subcortical volume were measured using a high-resolution image processing technique based on the Desikan-Killiany-Tourville atlas and an automated segmentation method (FreeSurfer version 6.0). Results After cortical reconstruction, in 7 tesla magnetic resonance imaging volume segmental analysis, compared with the healthy controls, the Parkinson’s disease patients showed global cortical atrophy, mostly in the prefrontal area (rostral middle frontal, superior frontal, inferior parietal lobule, medial orbitofrontal, rostral anterior cingulate area), and subcortical volume atrophy in limbic/paralimbic areas (fusiform, hippocampus, amygdala). Conclusion We first demonstrated that 7 tesla magnetic resonance imaging detects structural abnormalities in Parkinson’s disease patients compared to healthy controls using an automated segmentation method. Compared with the healthy controls, the Parkinson’s disease patients showed global prefrontal cortical atrophy and hippocampal area atrophy.


Author(s):  
Philon Nguyen ◽  
Thanh An Nguyen ◽  
Yong Zeng

AbstractDesign protocol data analysis methods form a well-known set of techniques used by design researchers to further understand the conceptual design process. Verbal protocols are a popular technique used to analyze design activities. However, verbal protocols are known to have some limitations. A recurring problem in design protocol analysis is to segment and code protocol data into logical and semantic units. This is usually a manual step and little work has been done on fully automated segmentation techniques. Physiological signals such as electroencephalograms (EEG) can provide assistance in solving this problem. Such problems are typical inverse problems that occur in the line of research. A thought process needs to be reconstructed from its output, an EEG signal. We propose an EEG-based method for design protocol coding and segmentation. We provide experimental validation of our methods and compare manual segmentation by domain experts to algorithmic segmentation using EEG. The best performing automated segmentation method (when manual segmentation is the baseline) is found to have an average deviation from manual segmentations of 2 s. Furthermore, EEG-based segmentation can identify cognitive structures that simple observation of design protocols cannot. EEG-based segmentation does not replace complex domain expert segmentation but rather complements it. Techniques such as verbal protocols are known to fail in some circumstances. EEG-based segmentation has the added feature that it is fully automated and can be readily integrated in engineering systems and subsystems. It is effectively a window into the mind.


2016 ◽  
Vol 29 (05) ◽  
pp. 386-393 ◽  
Author(s):  
Chiara Bergamino ◽  
Ruth Sanders ◽  
Ursula Fogarty ◽  
Antonella Puggioni ◽  
Clodagh Kearney ◽  
...  

Summary Objectives: To compare the accuracy and distribution of injectate for cranial (CR) and caudomedial (CM) ultrasound-guided injections of equine sacroiliac joints. Methods: Both sacroiliac joints from 10 lumbo sacropelvic specimens were injected using cranial parasagittal (CR; curved 18 gauge, 25 cm spinal needles) and caudomedial (CM; straight 18 gauge, 15 cm spinal needles) ultrasound-guided approaches. Injectate consisted of 4 ml iodinated contrast and 2 ml methylene blue. Computed tomo-graphical (CT) scans were performed before and after injections. Time for needle guidance and repositioning attempts were recorded. The CT sequences were analysed for accuracy and distribution of contrast. Results: Intra-articular contrast was detected in sacroiliac joints following 15/40 injections. The CR and CM approaches deposited injectate ≤ 2 cm from sacroiliac joint margins following 17/20 and 20/20 injections, respectively. Median distance of closest contrast to the sacroiliac joint was 0.4 cm (interquartile range [IQR]: 1.5 cm) for CR approaches and 0.6 cm (IQR: 0.95 cm) for CM approaches. Cranial injections resulted in injectate contacting lumbosacral intertrans-verse joints 15/20 times. Caudomedial injections were perivascular 16/20 times. Limitations: Safety and efficacy could not be established. Clinical relevance: Cranial and CM ultra-sound-guided injections targeting sacroiliac joints were very accurate for periarticular injection, but accuracy was poor for intra- articular injection. Injectate was frequently found in contact with interosseous sacroiliac ligaments, as well as neurovascular and synovial structures in close vicinity of sacroiliac joints.


UK-Vet Equine ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 150-157
Author(s):  
John David Stack ◽  
Jessica Harley

The sacroiliac joint and pain deriving from this complex region remains poorly understood in horses, although our understanding grows as the body of literature grows. A deeper understanding can be derived from the richer body of literature in human sacroiliac joint pain as the disease processes and biomechanics appear similar in both species. A highly specific and sensitive diagnostic test for this condition does not exist, so equine clinicians have to make presumptive diagnosis based on presenting signs, findings of clinical examination, diagnostic imaging and the response to blocking of the sacroiliac joint region. Many horses with sacroiliac joint region pain have concurrent orthopaedic injury or disease. Treatment is largely based on fundamentals, anecdotal evidence and translation of non-surgical techniques used in humans. Treatment for other orthopaedic conditions can conflict with rehabilitation for sacroiliac joint region pain, necessitating compromise.


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