

%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.
As the number of slices is a critical hyper-parameter on the recurrent methods presented (in nnUnet the software chooses the crops by itself), a specific study on that parameter is done. The results using several slices are in Table \ref{tab:th1} for proximal (P) and distal (D) occlusions. 

\begin{table}[!ht]
\vspace*{-0.075in}
 \centering
{\caption{Results using different numbers of slices for segmenting the thrombi using Att\acs{llstm} with CHSF and MATAR dataset. \label{tab:th1}}}

\vspace*{-0.05in}
 { \scalebox{0.75}{\begin{tabular}{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}}cc@{\hspace{-0.1ex}}c@{\hspace{-0.1ex}}c}
\toprule
Slices &\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

10 &1.3 &$\mid$ &0.9&74.1&$\mid$ &102.7& 0.3 &$\mid$& 0.3&292.7 &$\mid$& 79.4& 0.48& $\mid$ &0.51 &0.9 &$\mid$ &0.91 \\

 \textbf{12}&\textbf{1.0}& $\mid$& \textbf{0.6}&\textbf{49.8}&$\mid$&\textbf{63.3}&\textbf{ 0.2} &$\mid$& \textbf{0.3}&\textbf{0.2 }&$\mid$ &\textbf{79.4}& \textbf{0.55 }&$\mid$ &\textbf{0.54} &\textbf{1.0}& $\mid$& \textbf{0.91 }\\
14&3.3& $\mid$& 2.3&79.4&$\mid$ &82.8& 0.2 &$\mid$ &0.4&0.2&$\mid$& 108.5& 0.59& $\mid$ &0.42 &1.0& $\mid$& 0.85 \\

 
\bottomrule
\end{tabular}}}
\end{table}

Using $s$ equal to 12 for all the recurrent methods, as it produces the best result using AttLLSTM, we compare the results with some of the SOTA methods for the thrombi segmentation in Table \ref{tab:th}. nnUnet is trained using the general procedure and all our contributions are tested: first, just \ac{llstm} and CLSTM (adding at the beginning a CNN with 5$\times$5 filters instead of the attention), the attention module, and the two post-processing techniques (lesion and threshold (Thr)). As the other methods proposed for the thrombi use the hemisphere position \cite{hemisphere} or the reference path through the brainstem as annotations \cite{polarunet}, we do not include them.


 

\begin{table}[!ht]
\vspace*{-0.05in}
 \centering
{\caption{Results using different architectures and configurations for segmenting the thrombi using 12 slices for the recurrent methods. The best model is highlighted in bold and the best model without post-processing techniques is underlined. \label{tab:th}}}
\vspace*{-0.1in}
 { \scalebox{0.75}{\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 &CHSF+MATAR & & &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 & CHSF+MATAR & & &3.8& $\mid$& 2.6&178.1&$\mid$&153.5& 0.2& $\mid$ &0.4&0.2&$\mid$&28.1& 0.39 &$\mid$ &0.33 &1.0 &$\mid$ &0.81 \\ 

\ac{llstm} & CHSF+MATAR & & &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 \\ 


AttCLSTM &CHSF+MATAR & & &1.2& $\mid$&1.6 &43.81&$\mid$&99.3& 0.3& $\mid$ &0.4&141.1&$\mid$& 100.7& 0.41 &$\mid$ &0.38 &0.9&$\mid$ &0.72 \\ 





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 \\ 
\underline{Att\acs{llstm}} &CHSF+MATAR & &&\underline{1.0}& $\mid$& \underline{0.6}&\underline{49.8}&$\mid$&\underline{63.3}& \underline{0.2}&$\mid$& \underline{0.3}&\underline{0.2}&$\mid$ &\underline{79.4}& \underline{0.55} &$\mid$ &\underline{0.54} &\underline{1.0}& $\mid$& \underline{0.91} \\
nnUnet &CHSF+MATAR &\checkmark & &0.0& $\mid$& 0.7&0.0&$\mid$&161.3& 0.4 &$\mid$& 0.5&373.6&$\mid$&130.9& 0.48& $\mid$ &0.51&0.7 &$\mid$& 0.7\\ 
AttCLSTM &CHSF+MATAR &\checkmark & &0.5& $\mid$& 0.9&19.4&$\mid$&109.8& 0.3& $\mid$ &0.6&141.1&$\mid$&134.4 & 0.43 &$\mid$ &0.36&0.9&$\mid$ &0.6 \\
Att\acs{llstm} &CHSF+MATAR &\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 \\ 
nnUnet &CHSF+MATAR &\checkmark & \checkmark &0.0& $\mid$& 0.8&0.0&$\mid$&177.9& 0.5 &$\mid$& 0.5&373.6&$\mid$&130.9& 0.49& $\mid$ &0.51&0.7 &$\mid$& 0.7\\ 
AttCLSTM &CHSF+MATAR &\checkmark & \checkmark &0.5& $\mid$& 0.9&59.9&$\mid$&245.8& 0.3& $\mid$ &0.6&141.1&$\mid$&134.4 & 0.54&$\mid$ &0.32&0.9&$\mid$ &0.6 \\
\textbf{Att\acs{llstm}} &CHSF+MATAR &\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}}}
\end{table}

nnUnet struggles to segment the thrombi: less than 80$\%$ of the patients are detected. LLSTM outperforms CLSTM in the Dice score, even though the detection rate is lower. Adding to both recurrent methods the attention module increases the performance. AttLLSTM has the highest detection rate (having 91$\%$ in the smaller cases) and Dice. Without using the distal occlusions, AttLLSTM misses 40$\%$ of the patients and including them in the training improves all metrics. For the post-processing techniques, adding the lesion (Lesion) reduces the number of false positives in more than half of them but also reduces the detection rate for distal cases (some wrong close objects are chosen) for AttCLSTM. Finally, the threshold technique (Thr) allows to have on average a Dice of 0.43 or higher for all. AttLLSTM gets the best performance (0.61 as Dice) detecting almost all the patients (missing less than 10$\%$ of distal thrombi). Indeed, the model is already better without the post-processing comparing it with nnUnet/AttCLSTM +lesion+thr in all metrics. To test the robustness of the model, the control MRI segmentation results are present in Appendix \ref{appendix2}. Figure \ref{fig:ex} presents some predictions obtained, 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}
\vspace*{-0.2in} \centering
 \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 row is concerned with proximal thrombus, and the second one with distal thrombus.} 
\vspace*{-0.1in}
\subfloat[][SWAN]{\includegraphics[width=.2\linewidth]{img/th52.png}}\;
\subfloat[][SWAN zoom]{\includegraphics[width=.2\linewidth]{img/zoomth52.png}}\;
\subfloat[][Gt zoom]{\includegraphics[width=.2\textwidth]{img/zoomlabel52.png}} \;
\subfloat[][Pred zoom]{\includegraphics[width=.2\textwidth]{img/zoompred52.png}} \\
\subfloat[][SWAN]{\includegraphics[width=.2\textwidth]{img/th101.png}}\; 
\subfloat[][SWAN zoom]{\includegraphics[width=.2\textwidth]{img/zoomth101.png}} \;
\subfloat[][Gt zoom]{\includegraphics[width=.2\textwidth]{img/zoomlabel101.png}}\; 
\subfloat[][Pred zoom]{\includegraphics[width=.2\textwidth]{img/zoompred101.png}} 
\end{figure}

