Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The Left Ventricle LV undergoes remodelling after Myocardial Infarction MI. In order to quantify the remodelling status, clinicians make use of conventional measures, not fully exploiting the available shape information. To characterize the changes in heart shape and classify heart data as normal or infarcted,  use a hierarchical generative model, which jointly clusters shape point sets from LV in End-Systolic ED and End-Systolic ES phases, and estimates the probability density function pdf of each cluster.  use a Variational Bayes VB method to infer the clusters labels, the mean models, and variation modes for the clusters.  also present the results in the supervised setting, where the labels of training data sets are given. Our classification results are evaluated in terms of sensitivity, specificity, and accuracy using 200 LV shapes provided by MICCAI 2015 STACOM LV Statistical Shape Modelling Challenge. Our method successfully classifies the data, achieving a specificity of 0.92i¾?$$\pm $$i¾?0.06 and a sensitivity of 0.96i¾?$$\pm $$i¾?0.07 for the supervised learning approach, and a specificity of 0.83i¾?$$\pm $$i¾?0.03 and a sensitivity of 0.97i¾?$$\pm $$i¾?0.01 for the unsupervised learning approach.
['Catarina Pinto', 'Serkan Çimen', 'Ali Gooya', 'Karim Lekadir', 'Alejandro F. Frangi']
I am writing a research paper on a idea Joint Clustering and Component Analysis of Spatio-Temporal Shape Patterns in Myocardial Infarction
[{"from": "human", "value": "I am writing a research paper on a idea Joint Clustering and Component Analysis of Spatio-Temporal Shape Patterns in Myocardial Infarction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The Left Ventricle LV undergoes remodelling after Myocardial Infarction MI. In order to quantify the remodelling status, clinicians make use of conventional measures, not fully exploiting the available shape information. To characterize the changes in heart shape and classify heart data as normal or infarcted,  use a hierarchical generative model, which jointly clusters shape point sets from LV in End-Systolic ED and End-Systolic ES phases, and estimates the probability density function pdf of each cluster.  use a Variational Bayes VB method to infer the clusters labels, the mean models, and variation modes for the clusters.  also present the results in the supervised setting, where the labels of training data sets are given. Our classification results are evaluated in terms of sensitivity, specificity, and accuracy using 200 LV shapes provided by MICCAI 2015 STACOM LV Statistical Shape Modelling Challenge. Our method successfully classifies the data, achieving a specificity of 0.92i\u00be?$$\\pm $$i\u00be?0.06 and a sensitivity of 0.96i\u00be?$$\\pm $$i\u00be?0.07 for the supervised learning approach, and a specificity of 0.83i\u00be?$$\\pm $$i\u00be?0.03 and a sensitivity of 0.97i\u00be?$$\\pm $$i\u00be?0.01 for the unsupervised learning approach."}]