\begin{table}[t]
\centering
\caption{\small Comparison of segmentation models on the \emph{MSSEG-2} dataset, with progression subjects only. Values are mean (SD). \textbf{Bold} denotes best performance and \underline{underline} denotes second-best.}
\label{tab:model_comparison}
\scriptsize
\renewcommand{\arraystretch}{1.0}
\setlength{\tabcolsep}{4pt}
\begin{tabular}{l c c c c}
\toprule
\textbf{Model} & \textbf{Dim} & \textbf{Dice} $\uparrow$ & \textbf{Lesion-F1} $\uparrow$ & \textbf{PPV} $\uparrow$ \\
\midrule
SNAC & 3D & 0.531 & 0.307 & \textit{N/A} \\
SNAC (VNet) & 3D & 0.568 & 0.576 & \textit{N/A} \\
Neuropoly & 3D & 0.563 & 0.175 & \textit{N/A} \\
CoactSeg & 3D & \textbf{0.638} & 0.620 & \underline{0.636} \\
\midrule
DynUNet & 3D & 0.586 {\tiny(0.238)} & 0.622 {\tiny(0.276)} & \underline{0.636} {\tiny(0.325)} \\
LongiUNet-DW & 3D & 0.616 {\tiny(0.177)} & \underline{0.684} {\tiny(0.229)} & 0.569 {\tiny(0.216)} \\
SwinUNETR & 3D & 0.538 {\tiny(0.280)} & 0.557 {\tiny(0.330)} & \textbf{0.642} {\tiny(0.355)} \\
nnUNet & 3D & 0.615 {\tiny(0.202)} & 0.691 {\tiny(0.215)} & 0.585 {\tiny(0.248)} \\
% 2.5D MODELS
DynUNet & 2.5D & 0.527 {\tiny(0.222)} & 0.535 {\tiny(0.216)} & 0.453 {\tiny(0.255)} \\
SegFormer & 2.5D & 0.532 {\tiny(0.226)} & 0.518 {\tiny(0.308)} & 0.621 {\tiny(0.327)} \\
SegMaST (ours) & 2.5D & \underline{0.620} {\tiny(0.201)} & \textbf{0.723} {\tiny(0.235)} & 0.630 {\tiny(0.281)} \\
\bottomrule
\end{tabular}
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\end{table}