whiplash
发表于 2025-3-21 16:09:56
书目名称Computational Methods and Clinical Applications for Spine Imaging影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0232690<br><br> <br><br>书目名称Computational Methods and Clinical Applications for Spine Imaging影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0232690<br><br> <br><br>书目名称Computational Methods and Clinical Applications for Spine Imaging网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0232690<br><br> <br><br>书目名称Computational Methods and Clinical Applications for Spine Imaging网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0232690<br><br> <br><br>书目名称Computational Methods and Clinical Applications for Spine Imaging被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0232690<br><br> <br><br>书目名称Computational Methods and Clinical Applications for Spine Imaging被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0232690<br><br> <br><br>书目名称Computational Methods and Clinical Applications for Spine Imaging年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0232690<br><br> <br><br>书目名称Computational Methods and Clinical Applications for Spine Imaging年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0232690<br><br> <br><br>书目名称Computational Methods and Clinical Applications for Spine Imaging读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0232690<br><br> <br><br>书目名称Computational Methods and Clinical Applications for Spine Imaging读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0232690<br><br> <br><br>
Parabola
发表于 2025-3-21 22:46:51
What Is Clickbait? (Check All that Apply),east-squares regression. Despite its simplicity and without using specific domain knowledge, our approach achieves sub-voxel localisation accuracy of 0.61 mm, Dice segmentation overlaps of nearly 90% (for the training data) and takes less than ten minutes to process a new scan.
立即
发表于 2025-3-22 01:26:39
Michael R. Robinson,Moira M. Fergusonral bodies of the thoracic and lumbar spine. We evaluated osteophyte detection performance on 45 individuals with 5-fold cross validation and achieved state-of-the-art performance with 85% sensitivity at 2 false positive detections per patient.
刻苦读书
发表于 2025-3-22 06:22:49
G. T. O. Lebreton,F. W. (Bill) H. Beamishenerate the patient’s specified 3D model. This method just needs one prior model for 3D reconstruction. The experiments on nine vertebrae of three patients show the average reconstruction error is 1.2 mm (1.0 mm–1.3 mm) which is comparable to the state of the art.
Blazon
发表于 2025-3-22 12:03:53
Operator Theory: Advances and Applications on Grassmannian kernels in order to assess the similarity between shape topology and inter-vertebral poses in both groups (P, NP). We test the method to classify 52 progressive and 81 non-progressive patients enrolled in a prospective clinical study, yielding classification rates comparing favorably to standard classification methods.
CYN
发表于 2025-3-22 16:50:55
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CYN
发表于 2025-3-22 21:00:37
Accurate Intervertebral Disc Localisation and Segmentation in MRI Using Vantage Point Hough Forests east-squares regression. Despite its simplicity and without using specific domain knowledge, our approach achieves sub-voxel localisation accuracy of 0.61 mm, Dice segmentation overlaps of nearly 90% (for the training data) and takes less than ten minutes to process a new scan.
condescend
发表于 2025-3-22 23:19:49
Detection of Degenerative Osteophytes of the Spine on PET/CT Using Region-Based Convolutional Neuralral bodies of the thoracic and lumbar spine. We evaluated osteophyte detection performance on 45 individuals with 5-fold cross validation and achieved state-of-the-art performance with 85% sensitivity at 2 false positive detections per patient.
cutlery
发表于 2025-3-23 05:18:19
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OTHER
发表于 2025-3-23 06:11:27
Classification of Progressive and Non-progressive Scoliosis Patients Using Discriminant Manifolds on Grassmannian kernels in order to assess the similarity between shape topology and inter-vertebral poses in both groups (P, NP). We test the method to classify 52 progressive and 81 non-progressive patients enrolled in a prospective clinical study, yielding classification rates comparing favorably to standard classification methods.