

%We are going to show the results obtained using our database, as there is no public database with the same characteristics as ours. First, we present some short results for the lesion, but no deep study is done as it is used as support for the main purpose: the thrombi prediction, which results are shown after. We use as base model nnUnet \cite{nnUnet} and we compare with the architecture purpose adding each element.
\subsection{Lesion results}
In Table \ref{tab:restransfer} we can see the results for the lesion. nnUnet \cite{nnUnet} is used as a reference model, using exactly the training and validation procedure available in the library provided in \cite{nnunetcode} (just including our splits). It outperforms our proposal \ac{llstm} using just the proximal dataset (CHSF), probably due to all the preprocessing and post-processing incorporated in nnUnet that are not done in our model. So nnUnet is trained on both datasets, arriving at a Dice of 0.72 on average and close to a 100$\%$ percent detection rate. Notice that there is a considerable difference between the performances on the proximal (P)  and distal (D) datasets.  That model is used for the following experiments.

  \begin{table}[!ht]
 % The first argument is the label.
 % The caption goes in the second argument, and the table contents
 % go in the third argument.
  \centering
  {\caption{\label{tab:restransfer}Test set results using \ac{llstm} and nnUnet for lesion detection on both datasets.}}%  
\begin{tabular}{ccc@{\hspace{-0.1ex}}c@{\hspace{-0.1ex}}cc@{\hspace{-0.1ex}}c@{\hspace{-0.1ex}}cc@{\hspace{-0.1ex}}c@{\hspace{-0.1ex}}cc@{\hspace{-0.1ex}}c@{\hspace{-0.1ex}}cc@{\hspace{-0.1ex}}c@{\hspace{-0.1ex}}cc@{\hspace{-0.1ex}}c@{\hspace{-0.1ex}}c}
\toprule

Model &   Datasets &\multicolumn{6}{c}{False Positives}& \multicolumn{6}{c}{False Negatives} &\multicolumn{3}{c}{Dice}&\multicolumn{3}{c}{Det. } \\ \hspace{0.1in}
&& \multicolumn{3}{c}{Count} & \multicolumn{3}{c}{Size}  & \multicolumn{3}{c}{Count} &  \multicolumn{3}{c}{Size} & & \\
&& P&$\mid$&  D& P  & $\mid$ &   D & P  &$\mid$& D  &P  &$\mid$ &  D  & P &$\mid$& D  & P &$\mid$& D 
\\
\midrule
\ac{llstm}  & CHSF  &  0.8&$\mid$& & 82.8 &$\mid$& &1.4&$\mid$&  & 47.5 &$\mid$& & 0.70&$\mid$&  & 1.00&$\mid$&  \\ 
nnUnet & CHSF  &  3.7&$\mid$&  & 33.2&$\mid$&  & 1.9&$\mid$&  & 5.64 &$\mid$& & 0.75&$\mid$&  & 1.00&$\mid$&  \\


\textbf{nnUnet}  & Both  &  \textbf{1.1} &$\mid$&   \textbf{0.2}  & \textbf{19.3}  &$\mid$& \textbf{9.7} & \textbf{1.3} &$\mid$& \textbf{2.4} & \textbf{23.9} &$\mid$& \textbf{14.6}& \textbf{0.77}  &$\mid$& \textbf{0.67} & \textbf{1.00} &$\mid$&   \textbf{0.95} \\

\bottomrule
\end{tabular}

\end{table}


\subsection{Thrombi results}

For the thrombi, nnUnet is used as before and CLSTM \cite{ConvLSTM}. All our contributions are tested: first just \ac{llstm} (adding at the beginning a CNN with 5$\times$5 filters instead of the attention) then our final proposal Att\acs{llstm} and the two post-processing techniques (lesion and threshold (Thr)).  The results are summarized in Table \ref{tab:th}.
 

\begin{table}[!ht]
 % The first argument is the label.
 % The caption goes in the second argument, and the table contents
 % go in the third argument.
\caption{Results using different architectures and configurations for segmenting the thrombi. \label{tab:th}}%
  \scalebox{0.8}{\begin{tabular}{ccccc@{\hspace{-0.1ex}}c@{\hspace{-0.1ex}}cc@{\hspace{-0.1ex}}c@{\hspace{-0.1ex}}cc@{\hspace{-0.1ex}}c@{\hspace{-0.1ex}}cc@{\hspace{-0.1ex}}c@{\hspace{-0.1ex}}cc@{\hspace{-0.1ex}}c@{\hspace{-0.1ex}}cc@{\hspace{-0.1ex}}c@{\hspace{-0.1ex}}c}
\toprule

Model &   Datasets & Lesion & Thr &\multicolumn{6}{c}{False Positives}& \multicolumn{6}{c}{False Negatives} &\multicolumn{3}{c}{Dice}&\multicolumn{3}{c}{Det. } \\ \hspace{0.1in}
&&&& \multicolumn{3}{c}{Count} & \multicolumn{3}{c}{Size}  & \multicolumn{3}{c}{Count} &  \multicolumn{3}{c}{Size} & & \\
&&&& P&$\mid$&  D& P  & $\mid$ &   D & P  &$\mid$& D  &P  &$\mid$ &  D  & P &$\mid$& D  & P &$\mid$& D 
\\
\midrule




nnUnet  &Both&   & &0.6& $\mid$& 1.1&37.1&$\mid$&222.4& 0.4 &$\mid$& 0.5&373.6&$\mid$&125.3& 0.46& $\mid$ &0.45&0.7 &$\mid$& 0.7\\ 
CLSTM  & Both & & &3.83& $\mid$& 2.6&178.12&$\mid$&153.5& 0.2& $\mid$ &0.4&0.2&$\mid$&28.1& 0.39 &$\mid$ &0.33 &1.0 &$\mid$ &0.81 \\  

\ac{llstm} & Both & & &0.8& $\mid$& 1.7&111.9&$\mid$&34.9& 0.2& $\mid$ &0.6&0.2&$\mid$&136.4& 0.48 &$\mid$ &0.36 &1.0 &$\mid$ &0.64 \\   
Att\acs{llstm} & CHSF& &&3.8 &$\mid$ &3.0&111.9&$\mid$&101.5& 0.2 &$\mid$& 0.6&0.2 &$\mid$&136.4& 0.50 &$\mid$ &0.27 &1.0 &$\mid$& 0.64 \\  



Att\acs{llstm} &Both  &  &&1.0& $\mid$& 0.6&49.8&$\mid$&63.3& 0.2 &$\mid$& 0.3&0.2 &$\mid$ &79.4& 0.55 &$\mid$ &0.54 &1.0& $\mid$& 0.91 \\

Att\acs{llstm} &Both &\checkmark   & &0.0 &$\mid$ &0.1&0.0&$\mid$&8.5& 0.2 &$\mid$& 0.3&0.2&$\mid$&79.4& 0.52& $\mid$& 0.58 &1.0&  $\mid$ & 0.91 \\ 

\textbf{Att\acs{llstm}}   &Both &\checkmark   &\checkmark &\textbf{0.0}& $\mid$ &\textbf{0.1}&\textbf{0.0}&$\mid$&\textbf{8.5}& \textbf{0.2}& $\mid$ &\textbf{0.3}&\textbf{0.2}&$\mid$&\textbf{79.4}& \textbf{0.63}& $\mid$ &\textbf{0.59}&\textbf{1.0}& $\mid$ &\textbf{0.91} \\  
\bottomrule
\end{tabular}}
%\caption{Results using different architectures and configurations for segmenting the thrombi.  } \label{tab:th}
\end{table}

nnUnet struggles to segment the thrombi: only 70$\%$ are detected. For CLSTM and LLSTM, the Dice is considerably worse for the first model, in addition to the fact that CLSTM takes more time to train (as it has more parameters). The biggest improvement is seen with the inclusion of the attention module, allowing it to have the highest detection rate in difficult cases (distal ones). Without training with distal occlusions, the model misses  40$\%$ of the patients and including them in the training produces an improvement in all metrics. Finally, both post-processing metrics produce an improvement,  choosing the instance closer to the thrombi reduces the false positives to almost zero, and reducing the threshold for the prediction gives the best performance (0.61 using the Dice metric). The model detects almost all the patients (missing less than 10$\%$ of distal thrombi). 

Figure \ref{fig:ex} presents some predictions obtained with the best model, the original SWAN, zoomed, and predictions are shown, for a proximal thrombus (a,b,c,d) and distal one (e,f,g,h).

\begin{figure}[!ht]
\centering
%\floatconts  %
 \caption{\label{fig:ex}Visual prediction examples. Original slice and zoom versions are shown. The ground truth (Gt) and the predictions (pred) are shown in the zoomed version. The first thrombi is proximal and the second one is distal}
\begin{minipage}[b]{.2\linewidth}
    \centering
    \subcaption{\small (a) SWAN}
    \includegraphics[width=\textwidth]{img/th52.png}
  \end{minipage}
\hspace{0.1in}
\begin{minipage}[b]{.2\linewidth}
    \centering
    \subcaption{\small (b) SWAN (zoom)}    \includegraphics[width=\textwidth]{img/zoomth52.png}
  \end{minipage}
\hspace{0.1in}
\begin{minipage}[b]{.2\linewidth}
    \centering
    \subcaption{\small  (c) Gt (zoom)}
    \includegraphics[width=\textwidth]{img/zoomlabel52.png}
  \end{minipage}
\hspace{0.1in}
\begin{minipage}[b]{.2\linewidth}
    \centering
    \subcaption{\small (d) Pred (zoom)}   \includegraphics[width=\textwidth]{img/zoompred52.png}
  \end{minipage}
\\
\begin{minipage}[b]{.2\linewidth}
    \centering
    \subcaption{\small (e) SWAN}
    \includegraphics[width=\textwidth]{img/th101.png}
  \end{minipage}
\hspace{0.1in}
\begin{minipage}[b]{.2\linewidth}
    \centering
    \subcaption{\small (f) SWAN (zoom)}    \includegraphics[width=\textwidth]{img/zoomth101.png}
  \end{minipage}
\hspace{0.1in}
\begin{minipage}[b]{.2\linewidth}
    \centering
    \subcaption{\small  (g) Gt (zoom)}
    \includegraphics[width=\textwidth]{img/zoomlabel101.png}}
  \end{minipage}
\hspace{0.1in}
\begin{minipage}[b]{.2\linewidth}
    \centering
    \subcaption{\small (h) Pred (zoom)}   \includegraphics[width=\textwidth]{img/zoompred101.png}
  \end{minipage}
\end{figure}
