\section{Deep Learning methods are sensitive to preprocessing choices}
\label{sec:preprocessing}
An often overlooked limitation of deep learning methods is their sensitivity to preprocessing choices.
Most deep learning methods are trained on a fixed set of preprocessing steps, and may perform poorly if the preprocessing steps are not the same as the ones used during training.
In contrast to highly controlled evaluation environments like registration challenges, real-world data such as \textit{ex-vivo} hemispheres, histology, and blockface images are rarely standardized to the same preprocessing steps or stereotaxic coordinates.
Other domains like MRA imaging can have limited field of view and are highly anisotropic, making it difficult to standardize the preprocessing steps across modalities.
Sensitivity to preprocessing choices shifts the burden of preprocessing from the model to the practitioner, who may not be familiar with the preprocessing protocol used during training and might produce suboptimal results.
% Moreover, if the deep learning model is sensitive to preprocessing choices, arbitrarily chosen preprocessing steps can lead to spurious results by a practitioner who may not be familiar with the preprocessing protocol used during training.

\textbf{Evaluation}.
To demonstrate the sensitivity of state-of-the-art deep learning method VFA to preprocessing choices, we perform an ablation study on the NIMH dataset using the SynthSeg segmentation protocol.
The NIMH dataset originally contains $208\times256\times256$ voxels when resampled to 1mm isotropic resolution.
Our preliminary experiments with VFA on the original 1mm isotropic T1w images resulted in significantly worse performance than expected. 
Upon further investigation, we found that VFA performs registration well only if the images are cropped to $192\times160\times224$ voxels.
Therefore, we cropped the images to a smaller region of interest (ROI) of $192\times160\times224$ voxels and evaluated the performance of VFA on these cropped images, upon which we obtained significantly better performance.
We also note that VFA was trained on images oriented in RAS frame, and therefore evaluated its performance on the DICOM-standard LPS orientation.

\textbf{Results}.
Our results in \autoref{fig:ablation-nimh} show that VFA performs significantly better on the cropped images across all modalities, and that the performance is significantly worse on the original images, implying that the model is `locked in' to a particular voxel size.
% This is a problematic aspect in case the image does not fit inside the field of view of the model
This poses a practical limitation wherein the practitioner may not be able to use the model if the anatomy of interest does not fit inside the field of view of the cropped image.
% 
In contrast, iterative methods suffer from no such limitation, and can be readily used by the practitioner without worrying about esoteric preprocessing choices.
Fortunately, there is no significant difference in performance by changing the orientation of the images, demonstrating some task understanding and generalization by the model.

\begin{figure}[t!]
    \centering
    \begin{minipage}{0.49\linewidth}
        \includegraphics[width=\linewidth]{figures/nimh_t1_crop.pdf}
    \end{minipage}
    \begin{minipage}{0.49\linewidth}
        \includegraphics[width=\linewidth]{figures/nimh_ood_crop.pdf}
    \end{minipage}
    % \caption{\small Ablation study on the NIMH dataset showing the effect of preprocessing choices on the performance of the model.}
    \caption{\small Ablation study on the NIMH dataset showing the effect of preprocessing choices on the performance of the model.
    \textbf{Left} shows the performance of VFA on the cropped images, on the original images (denoted as \textit{no crop}), and on images in the LPS orientation (denoted as \textit{LPS}) on the T1w modality. \textbf{Right} shows the performance of VFA on the cropped and original images on the T2w, T2*, and FLAIR modalities.
    }
    \label{fig:ablation-nimh}
\end{figure}
