\begin{table}[t]
\centering
\caption{\small Comparison of segmentation models for baseline (B), progression (P), and no progression (NP) mask prediction in glioma cases. Values are mean (SD). \textbf{Bold} and \underline{underline} denote best and second-best. * and ** indicate statistical differences to \emph{SegMaST} using a paired t-test with significance values $<$0.05 and $<$0.01, respectively. Training ratio is computed from wall-clock training time (in hours) per fold on a single GPU, normalized to \emph{SegMaST}.}
\label{tab:ucsf_results}

\scriptsize
\renewcommand{\arraystretch}{1.05}
\setlength{\tabcolsep}{5pt}

\begin{tabular}{l c c c c c}
    \toprule
    \multirow{2}{*}{\textbf{Model}} &
    \multirow{2}{*}{\textbf{Dim}} &
    \multicolumn{3}{c}{\textbf{Dice} $\uparrow$} &
    \multirow{2}{*}{\textbf{Train Ratio $\downarrow$}} \\
    \cmidrule(lr){3-5}
    & & \textbf{B} & \textbf{P} & \textbf{NP} & \\
    \midrule

    DynUNet & 3D 
        & 0.846 {\tiny(0.092)}
        & \textbf{0.520} {\tiny(0.096)}
        & 0.125 {\tiny(0.331)}**
        & 2.60 \\

    LongiUNet-DW & 3D 
        & 0.836 {\tiny(0.096)}
        & 0.496 {\tiny(0.141)}
        & 0.000 {\tiny(0.000)}**
        & 2.73 \\
    
    Swin UNETR & 3D 
        & 0.784 {\tiny(0.153)}*
        & 0.449 {\tiny(0.131)}
        & 0.063 {\tiny(0.242)}**
        & 3.13 \\

nnUNet & 3D 
    & \textbf{0.861} {\tiny(0.117)}*
    & 0.508 {\tiny(0.095)}
    & 0.063 {\tiny(0.242)}**
    & 3.97 \\

    
    DynUNet & 2.5D
        & 0.839 {\tiny(0.090)}
        & 0.493 {\tiny(0.151)}
        & 0.563 {\tiny(0.496)}
        & 1.47 \\

    SegFormer & 2.5D 
        & 0.791 {\tiny(0.126)}*
        & 0.497 {\tiny(0.128)}
        & \underline{0.500} {\tiny(0.500)}*
        & 1.13 \\

    SegMaST (ours) & 2.5D 
        & \underline{0.844} {\tiny(0.075)}
        & \underline{0.511} {\tiny(0.132)}
        & \textbf{0.750} {\tiny(0.433)}
        & \textbf{1.00} \\

    \bottomrule
\end{tabular}
\renewcommand{\arraystretch}{1.0}
\end{table}
