moving average scheme can vary based on the radial proximity of the data point to the vertical projection of the apex on the base (as seen in LA apical sections Fig. \[r-theta\]) as follows: $$k(i)=k_s+(k_l-k_s)\times \frac{r_l-r(i)}{r_l-r_s}
\label{span}$$ where $k(i)$, $k_l$, $k_s$, $r_l$, $r_s$ and $r(i)$ are the span value on the $i^{th}$ data point, the larger span, the smaller span, the largest $r$, the smallest $r$ and the radial distance of the $i^{th}$ data point from the mid-point of the endpoints of the dataset, respectively. Equation \[span\] basically interpolates the value of span in a way to have a larger span near the base and a smaller span near the apex.

Scaling, positioning and orientation of the LV sections {#Scaling}
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The left ventricular end-diastolic dimension (LVEDD) is chosen as the scaling parameter to scale all the segmented data. The algorithm employed for fitting the SA and LA sections in our previous work [@rajan2016automated] can be seen in Fig. \[optimization\]. The general algorithm has been kept the same with changes only to the positioning of the SA sections and the apex. Rajan et al. [@rajan2016automated] assumed that the base is fixed while the apex moves in space and for the 