Algorithm 1: 2D-to-3D framework for semantic segmentation
Input: Segmented projection tilt series $\left\{seg\left(V,\theta \right)|\theta \in \Theta \right\}$
Initialization: $M=zeros$(L,W,H)
Procedure Backward projection
For each $\theta \in \Theta$ do
$M=M+BP\left(seg\left(V,\theta \right)\right)$
end
Procedure Threshold autoselection
For each proposed threshold $T\left(i\right)$ do
$M_{T\left(i\right)}=1\left(M>T\left(i\right)\right)$;
score(i) = ${\sum }_{\theta \in \Theta }IoU\left(seg\left(V,\theta \left(i\right)\right),FP\left(M_{T\left(i\right)},\theta \right)\right)$
end
return segmented tomogram $M_{3D}=M_{T\left(i\right)}$ with the highest score(i).