Unbiased stratification of left ventricles.
Image based quantitative stratification of the Left Ventricles (LV) across a population helps in unraveling the structure-function symbiosis of the heart. An unbiased, reference less grouping scheme that automatically determines the number of clusters and a physioanatomically relevant strategy that aligns the intra cluster LV shapes would enable the robust construction of pathology stratified cardiac atlas. This paper achieves this hitherto elusive stratification and alignment by adapting the conventional strategies routinely followed by clinicians. The individual LV shape models (N=127) are independently oriented to an "attitudinally consistent orientation" that captures the physioanatomic variations of the LV morphology. Affinity propagation technique based on the automatically identified inter-LV_landmark distances is used to group the LV shapes. The proposed algorithm is computationally efficient and, if the inter cluster variations are linked to pathology, could provide a clinically relevant cardiac atlas.
Srinivasan
Rajagopalan
R
Imaging Technologies Lab, GE Global Research, India.
Shriram
K S
KS
Suryanarayanan
Srikanth
S
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D006801:Humans; D008955:Models, Cardiovascular; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*; D018487:Ventricular Dysfunction, Left / Q000000981:diagnostic imaging*