

% \begin{table}[h]
%     \centering

    
%     \caption{TODO: We highlight the best performance with bold and the second best with underline.} % Add table caption here
%     \label{tab:agreement_metrics}


%     % \renewcommand{\arraystretch}{1.3} % Adjust row height for better readability

%     \begin{tabular}{l ccc c ccc}
%         \toprule
%         \textbf{Model} & \multicolumn{3}{c}{\textbf{Inter-Subject}} & & \multicolumn{3}{c}{\textbf{Intra-Subject}} \\
%         \cline{2-4} \cline{6-8}
%         & \textbf{ICC} & \textbf{Kappa} & \textbf{Dice} & & \textbf{ICC} & \textbf{Kappa} & \textbf{Dice} \\
%         \midrule
%         SRENet & 
%         0.91$\pm$0.02 & 0.90$\pm$0.02 & 0.91$\pm$0.03 & & 0.92$\pm$0.04 & 0.90$\pm$0.02 & 0.90$\pm$0.03 \\
%         E2CNN  & 
%         0.89$\pm$0.03 & 0.85$\pm$0.06 & 0.86$\pm$0.06 & &
%         0.86$\pm$0.06 & 0.80$\pm$0.04 & 0.81$\pm$0.04 \\
%         ResNet & 
%         0.83$\pm$0.01 & 0.82$\pm$0.05 & 0.83$\pm$0.06 & &
%         0.85$\pm$0.03 & 0.82$\pm$0.05 & 0.82$\pm$0.06 \\
%         \bottomrule
%     \end{tabular}
% \end{table}


\begin{table}[!t]
\centering
\caption{\textbf{Unsupervised Segmentation Quantitative Evaluation.} 
Intra- and inter-subject segmentation performance (mean $\pm$ standard deviation) using equivariant learning (SRENet and E2CNN) and standard convolution (ResNet) evaluated with ICC, Kappa, and Dice.
We highlight the best performance with bold. } % Add table caption here
\label{tab:agreement_metrics}

\resizebox{\linewidth}{!}
{
{
\begin{tabular}{lcccccc}
    \toprule
    % \multicolumn{1}{c}{\multirow{2}{*}{\textbf{Method}}} 
    \multirow{2}{*}{\textbf{Model}} 
    & \multicolumn{3}{c}{\textbf{Intra-Subject}} & \multicolumn{3}{c}{\textbf{Inter-Subject}}\\ 
    \cmidrule(l){2-4} \cmidrule(l){5-7}
    & \textbf{ICC} & \textbf{Kappa} & \textbf{Dice} & \textbf{ICC} & \textbf{Kappa} & \textbf{Dice} \\ 
    \midrule
    SRENet & \textbf{0.92$\pm$0.04} & \textbf{0.90$\pm$0.02} & \textbf{0.90$\pm$0.03} & \textbf{0.91$\pm$0.02} & \textbf{0.90$\pm$0.02} & \textbf{0.91$\pm$0.03} \\ 
    E2CNN & 0.86$\pm$0.06 & 0.80$\pm$0.04 & 0.81$\pm$0.04 & 0.89$\pm$0.03 & 0.85$\pm$0.06 & 0.86$\pm$0.06 \\
    ResNet & 0.85$\pm$0.03 & 0.82$\pm$0.05 & 0.82$\pm$0.06 & 0.83$\pm$0.01 & 0.82$\pm$0.05 & 0.83$\pm$0.06\\
    \bottomrule
\end{tabular}
}
}
\end{table}


