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\newcommand\antsYang{ANTs~C\xspace}
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\begin{document}
\appendix
\section{Supplementary Notes}

\subsection{Reference image overlay}
\label{sec:reg_img_overlay}

\Cref{fig:refVisOverlays} shows two examples of test images
(randomly selected from the $20$ test images) aligned to four
different templates using the ``best'' algorithm (\ants A above). Some
differences are visible upon close inspection. For example, the right
mushroom body is poorly aligned for Fly B using the \fcwb template.
Small misalignments near the mushroom body are evident for both flies
after registration to \jfrc\,2013. Otherwise, alignment appears generally
good.

\begin{figure}
  \includegraphics[width=\textwidth]{refOverlays.pdf}
  \caption{Overlays of the reference (nc82) channel. Scale bar $100$\,\micron.}
  \label{fig:refVisOverlays}
\end{figure}

\subsection{Algorithm comparison}
\label{sec:sup_alg}

\Cref{tab:avg_dist_alg} compares the eight different registration
algorithms we tested by tabulating statistics of the skeleton distance
described above in Section~4.1. \antsDog performed
best overall, though the two parameter settings of \elastix were
similarly good according to these measures.  The two worst performing
algorithms were \antsYang, the least smooth parameter settings among
\ants algorithms, and \cmtkCow the smoothest of the \cmtk algorithms.

\begin{table}[H]
\centering

  \begin{tabular}{llrrrr}
\toprule
 & \multicolumn{2}{c}{Mean distance} & Mean of & \\
Algorithm & JRC 2018 & All &  medians & Mean rank\\
\midrule
    ANTs A &           3.97 &                 4.56 &             3.48 &       1.80 \\
 Elastix A &           4.05 &                 4.50 &             3.53 &       2.20 \\
 Elastix B &           4.13 &                 4.53 &             3.54 &       2.80 \\
    ANTs B &           4.34 &                 4.68 &             3.71 &       3.20 \\
    CMTK A &           4.03 &                 5.08 &             3.78 &       4.20 \\
    CMTK B &           4.04 &                 4.99 &             3.70 &       4.20 \\
    ANTs C &           4.53 &                 4.87 &             3.83 &       4.60 \\
    CMTK C &           4.13 &                 5.07 &             3.90 &       5.00 \\
\bottomrule
\end{tabular}
\caption{
    Summaries of algorithm performance, tabulating mean skeleton
    distance values across templates of the summary statistics given in
    \Cref{tab:alg_template_pairs}, i.e., mean-of-means, mean-of-medians,
    and mean-of-ranks. Mean rank is computed by ranking the algorithms
    by mean skeleton distance when the template is fixed, then averaging
    over templates.  Algorithms are sorted by their mean skeleton
    distance for the \jrc\,2018 template.
    }
\label{tab:avg_dist_alg}
\end{table}


\subsection{Size and shape normalization for evaluation}
 
\Cref{fig:distJacScatterAffNorm} shows the results of mean
skeleton distance and Jacobian determinant standard deviation (\jsd)
after normalization by using only the affine part of the bridging
transformation. This is 
in contrast to Fig.~5, in which both the affine
and deformable parts of the transformation contributed to normalization.
We observed that results are generally very similar. The largest
difference we observe is that the distance measures for the \fcwb
template are notably larger (worse performance) when using
affine-only normalization.

\begin{figure}
  \includegraphics[width=\textwidth]{distAffineNorm_vs_jacstd_colors_20180613_ed-preprint.pdf}
  \caption{Scatterplot showing mean skeleton distance and standard
  deviation of Jacobian transform map for template-algorithm pairs using
  affine-only normalization.}
  \label{fig:distJacScatterAffNorm}
\end{figure}

\subsection{Effect of resolution}
\label{sec:supp_res}

We performed experiments to explore the effect that the resolution of a
template has on the tradeoff between registration quality and
computational cost. We repeated the evaluation described in
Section~4.2 using downsampled versions of the two best
performing templates: \jrc\,2018 and Tefor. We resampled each of these
two templates from its native resolution to three lower, isotropic
resolutions: 1.2, 2.4, and 3.6\,\micron. These resolutions correspond to
downsampling in $xy$ by factors of approximately 2, 3, and 4.
\Cref{fig:distCpuByResolution} shows a scatterplot of mean skeleton distance
(our measure of registration accuracy) against mean CPU time for
template-algorithm pairs at various resolutions. The same results are
listed in \Cref{tab:distCpuResolution}.

\begin{figure}
  \begin{tabular}{c}
     \includegraphics[width=\textwidth]{res_speed_quality_ed_20190827.pdf} \\
     \includegraphics[width=\textwidth]{res_speed_quality_best_ed_20190909.pdf}
  \end{tabular}
  \caption{Scatterplot showing mean skeleton distance and the mean
    computation time in CPU-hours for the \jrc\,2018 and Tefor templates
    at various resolutions (above), and the best performing pairs (below).}
  \label{fig:distCpuByResolution}
\end{figure}

We observed that some downsampled templates
perform as well or nearly as well as the full-resolution templates.
For instance, \jrc\,2018, sampled at 1.2\,\micron performs competitively 
with the full-resolution template using \cmtkCOG, \cmtkHideo, and \elastixSFA.
Downsampled versions of Tefor seem to suffer somewhat more in terms of
performance, though are competitive when using \elastixSFA or \antsOwl.
Detailed exploration of algorithm parameters will influence the
time-per\-formance tradeoff, and may be worthwhile for some tasks. Here we
give values for reasonable ranges of parameters from faster and more
highly regularized to slower with potentially greater accuracy.

More computationally expensive algorithms tend to enjoy larger
speed\-ups when operating on downsampled images. For example, using
\jrc~2018, \antsDog (the slowest) speeds up by six times on downsampled
by two images relative to full resolution, whereas \elastixSFA (the
fastest), speeds up by two times.



Furthermore, more highly regularized \ants parameter settings may perform better
on very highly downsampled images.  For example, \antsOwl is the best
performing algorithm on the most downsampled versions of both templates.
This does not seem to be the case for \cmtk, since \cmtkCow performs
relatively poorly for the most downsampled template versions.

\begin{table}[H]
\tiny\centering

  \begin{tabular}{llrr}
\toprule
Template &  Algorithm &  Mean Distance (um) &  Mean CPU time (hrs) \\
\midrule
         JRC2018 &     ANTs A &                3.97 &               145.48 \\
                 &     ANTs B &                4.34 &                33.96 \\
                 &     ANTs C &                4.53 &               109.43 \\
                 &     CMTK A &                4.03 &                12.83 \\
                 &     CMTK B &                4.04 &                 4.02 \\
                 &     CMTK C &                4.13 &                 3.61 \\
                 &  Elastix A &                4.05 &                 0.22 \\
                 &  Elastix B &                4.13 &                 0.34 \\
JFRC2018 (1.2um) &     ANTs A &                4.65 &                23.10 \\
                 &     ANTs B &                4.11 &                 4.71 \\
                 &     ANTs C &                4.27 &                 3.69 \\
                 &     CMTK A &                4.04 &                 4.35 \\
                 &     CMTK B &                4.06 &                 1.17 \\
                 &     CMTK C &                4.15 &                 1.18 \\
                 &  Elastix A &                4.09 &                 0.11 \\
                 &  Elastix B &                4.30 &                 0.19 \\
JFRC2018 (2.4um) &     ANTs A &                4.53 &                 3.23 \\
                 &     ANTs B &                4.11 &                 4.73 \\
                 &     ANTs C &                4.29 &                 0.47 \\
                 &     CMTK A &                4.55 &                 2.36 \\
                 &     CMTK B &                4.11 &                 0.76 \\
                 &     CMTK C &                4.25 &                 0.60 \\
                 &  Elastix A &                4.22 &                 0.08 \\
                 &  Elastix B &                4.79 &                 0.20 \\
JFRC2018 (3.6um) &     ANTs A &                4.59 &                 1.12 \\
                 &     ANTs B &                4.22 &                 5.17 \\
                 &     ANTs C &                4.52 &                 0.18 \\
                 &     CMTK A &                4.73 &                 1.71 \\
                 &     CMTK B &                4.70 &                 0.57 \\
                 &     CMTK C &                5.20 &                 0.48 \\
                 &  Elastix A &                4.54 &                 0.08 \\
                 &  Elastix B &                5.34 &                 0.19 \\
           Tefor &     ANTs A &                4.00 &               113.92 \\
                 &     ANTs B &                4.50 &                 6.05 \\
                 &     ANTs C &                4.83 &                21.65 \\
                 &     CMTK A &                5.44 &                18.19 \\
                 &     CMTK B &                5.45 &                 8.99 \\
                 &     CMTK C &                5.27 &                10.04 \\
                 &  Elastix A &                4.12 &                 0.20 \\
                 &  Elastix B &                4.59 &                 0.28 \\
   Tefor (1.2um) &     ANTs A &                4.51 &                19.58 \\
                 &     ANTs B &                4.23 &                 3.82 \\
                 &     ANTs C &                4.44 &                 3.28 \\
                 &     CMTK A &                4.88 &                 5.17 \\
                 &     CMTK B &                4.89 &                 2.36 \\
                 &     CMTK C &                5.33 &                 2.14 \\
                 &  Elastix A &                4.16 &                 0.10 \\
                 &  Elastix B &                4.83 &                 0.22 \\
   Tefor (2.4um) &     ANTs A &                4.56 &                 2.98 \\
                 &     ANTs B &                4.12 &                 5.21 \\
                 &     ANTs C &                4.34 &                 0.53 \\
                 &     CMTK A &                5.44 &                 2.57 \\
                 &     CMTK B &                5.47 &                 1.12 \\
                 &     CMTK C &                6.02 &                 1.04 \\
                 &  Elastix A &                4.33 &                 0.09 \\
                 &  Elastix B &                5.27 &                 0.19 \\
   Tefor (3.6um) &     ANTs A &                5.11 &                 1.29 \\
                 &     ANTs B &                4.17 &                 5.94 \\
                 &     ANTs C &                4.52 &                 0.19 \\
                 &     CMTK A &                5.14 &                 1.75 \\
                 &     CMTK B &                5.64 &                 0.61 \\
                 &     CMTK C &                6.36 &                 0.66 \\
                 &  Elastix A &                4.85 &                 0.09 \\
                 &  Elastix B &                5.97 &                 0.19 \\
\bottomrule
\end{tabular}
  \caption{ Mean skeleton distances and mean CPU time by template and
  algorithm at different resolutions.}
  \label{tab:distCpuResolution}
\end{table}

\begin{table}[H]
\scriptsize\centering

  \begin{tabular}{llrrr}
\toprule
Template & Algorithm  &  Mean &  Std dev &  Median \\
\midrule
 JRC2018 &     ANTs A &           3.97 &          3.65 &             3.08 \\
   Tefor &     ANTs A &           4.00 &          3.68 &             3.16 \\
 JRC2018 &     CMTK A &           4.03 &          3.70 &             3.16 \\
 JRC2018 &     CMTK B &           4.04 &          3.70 &             3.16 \\
 JRC2018 &  Elastix A &           4.05 &          3.73 &             3.24 \\
JFRC2013 &  Elastix A &           4.06 &          3.68 &             3.24 \\
JFRC2013 &  Elastix B &           4.10 &          3.73 &             3.24 \\
JFRC2010 &  Elastix A &           4.11 &          3.68 &             3.24 \\
   Tefor &  Elastix A &           4.12 &          3.69 &             3.24 \\
 JRC2018 &  Elastix B &           4.13 &          3.78 &             3.24 \\
 JRC2018 &     CMTK C &           4.13 &          3.74 &             3.24 \\
JFRC2013 &     ANTs A &           4.15 &          3.84 &             3.24 \\
 JRC2018 &     ANTs B &           4.34 &          3.94 &             3.46 \\
    FCWB &     ANTs A &           4.35 &          3.97 &             3.46 \\
JFRC2010 &  Elastix B &           4.42 &          3.95 &             3.54 \\
   Tefor &     ANTs B &           4.50 &          4.02 &             3.61 \\
 JRC2018 &     ANTs C &           4.53 &          4.07 &             3.61 \\
   Tefor &  Elastix B &           4.59 &          4.13 &             3.61 \\
JFRC2010 &     ANTs B &           4.59 &          4.06 &             3.61 \\
JFRC2013 &     CMTK A &           4.73 &          4.71 &             3.61 \\
JFRC2010 &     ANTs C &           4.81 &          4.15 &             3.81 \\
   Tefor &     ANTs C &           4.83 &          4.32 &             3.81 \\
JFRC2013 &     CMTK B &           4.85 &          4.89 &             3.61 \\
JFRC2013 &     ANTs B &           4.97 &          4.40 &             3.81 \\
JFRC2013 &     ANTs C &           4.98 &          4.41 &             3.81 \\
    FCWB &     ANTs B &           5.00 &          4.26 &             4.06 \\
JFRC2013 &     CMTK C &           5.10 &          4.23 &             4.12 \\
    FCWB &     CMTK C &           5.17 &          4.54 &             4.06 \\
    FCWB &     ANTs C &           5.21 &          4.41 &             4.12 \\
    FCWB &     CMTK B &           5.26 &          4.75 &             4.06 \\
   Tefor &     CMTK C &           5.27 &          5.44 &             3.81 \\
JFRC2010 &     CMTK B &           5.36 &          5.13 &             3.87 \\
    FCWB &  Elastix B &           5.40 &          5.59 &             4.06 \\
   Tefor &     CMTK A &           5.44 &          5.75 &             3.81 \\
   Tefor &     CMTK B &           5.45 &          5.74 &             3.81 \\
    FCWB &     CMTK A &           5.52 &          4.97 &             4.18 \\
JFRC2010 &     CMTK A &           5.69 &          5.50 &             4.12 \\
JFRC2010 &     CMTK C &           5.69 &          5.28 &             4.24 \\
    FCWB &  Elastix A &           6.18 &          5.93 &             4.69 \\
JFRC2010 &     ANTs A &           6.31 &          6.20 &             4.47 \\
\bottomrule
\end{tabular}
    \caption{ Skeleton distance statistics (in \micron) for all algorithm/template
    pairs, sorted by mean distance.}
  \label{tab:alg_template_pairs}
\end{table}

\subsection{Deformation measure}
\label{sec:sup_defMeasure}

\Cref{tab:jacdet_mnStd} shows the mean and standard deviations of
the distributions of the Jacobian determinants for all templates and
algorithms. As expected, the means for all methods are generally close
to 1.0, and the standard deviations are smaller for algorithms with
stronger regularization (\antsOwl\ and \cmtkCow). This is in line with
our reasoning above, suggesting that the standard deviation
of the Jacobian determinant maps is a more informative statistic than the
mean.

\begin{table}[H]
\scriptsize\centering

  \begin{tabular}{llrr}
\toprule
         &            &  Jacobian determinant &  Hessian frobenius norm \\
Template &  Algorithm &  Mean (std dev)  &  Mean (std dev) \\
\midrule
JFRC2013 &     ANTs A &  0.98 (0.23) &  0.039 (0.033) \\
JFRC2013 &     ANTs B &  0.98 (0.11) &  0.013 (0.013) \\
JFRC2013 &     ANTs C &  0.99 (0.32) &  0.062 (0.058) \\
JFRC2013 &     CMTK A &  1.02 (0.26) &  0.017 (0.016) \\
JFRC2013 &     CMTK B &  1.01 (0.23) &  0.013 (0.013) \\
JFRC2013 &     CMTK C &  1.00 (0.09) &  0.0018 (0.0016) \\
JFRC2013 &  Elastix A &  1.01 (0.23) &  0.013 (0.013) \\
JFRC2013 &  Elastix B &  1.01 (0.23) &  0.013 (0.013) \\
\midrule
JFRC2010 &     ANTs A &  0.99 (0.26) &  0.032 (0.028) \\
JFRC2010 &     ANTs B &  0.97 (0.12) &  0.0124 (0.011) \\
JFRC2010 &     ANTs C &  0.95 (0.30) &  0.049 (0.045) \\
JFRC2010 &     CMTK A &  1.03 (0.19) &  0.014 (0.015) \\
JFRC2010 &     CMTK B &  1.04 (0.17) &  0.013 (0.014) \\
JFRC2010 &     CMTK C &  1.01 (0.05) &  0.0019 (0.0021) \\
JFRC2010 &  Elastix A &  1.02 (0.15) &  0.0029 (0.0023) \\
JFRC2010 &  Elastix B &  1.03 (0.29) &  0.011 (0.0083) \\
\midrule
   Tefor &     ANTs A &  1.01 (0.26) &  0.041 (0.034) \\
   Tefor &     ANTs B &  0.97 (0.11) &  0.023 (0.020) \\
   Tefor &     ANTs C &  0.97 (0.31) &  0.053 (0.050) \\
   Tefor &     CMTK A &  1.04 (0.23) &  0.019 (0.019) \\
   Tefor &     CMTK B &  1.03 (0.21) &  0.016 (0.017) \\
   Tefor &     CMTK C &  1.03 (0.08) &  0.0039 (0.0050) \\
   Tefor &  Elastix A &  1.02 (0.14) &  0.0033 (0.0024) \\
   Tefor &  Elastix B &  1.02 (0.30) &  0.013 (0.010) \\
\midrule
 JRC2018 &     ANTs A &  1.01 (0.21) &  0.035 (0.029) \\
 JRC2018 &     ANTs B &  0.98 (0.11) &  0.017 (0.015) \\
 JRC2018 &     ANTs C &  0.98 (0.27) &  0.047 (0.042) \\
 JRC2018 &     CMTK A &  1.03 (0.21) &  0.025 (0.023) \\
 JRC2018 &     CMTK B &  1.03 (0.17) &  0.018 (0.017) \\
 JRC2018 &     CMTK C &  1.02 (0.10) &  0.0068 (0.0069) \\
 JRC2018 &  Elastix A &  1.03 (0.17) &  0.018 (0.017) \\
 JRC2018 &  Elastix B &  1.03 (0.17) &  0.018 (0.017) \\
\midrule
    FCWB &     ANTs A &  1.00 (0.47) &  0.051 (0.054) \\
    FCWB &     ANTs B &  0.98 (0.13) &  0.0267 (0.023) \\
    FCWB &     ANTs C &  0.97 (0.24) &  0.056 (0.055) \\
    FCWB &     CMTK A &  1.06 (0.32) &  0.038 (0.035) \\
    FCWB &     CMTK B &  1.05 (0.28) &  0.033 (0.031) \\
    FCWB &     CMTK C &  1.01 (0.09) &  0.0097 (0.012) \\
    FCWB &  Elastix A &  1.08 (0.23) &  0.0047 (0.0035) \\
    FCWB &  Elastix B &  1.11 (0.39) &  0.014 (0.011) \\
\bottomrule
\end{tabular}
    \caption{Mean (Standard deviation) of Jacobian determinant standard
      deviation and Hessian frobenius norm mean by template. Observe
      that all mean values are close to 1.0, whereas standard deviations
      vary much more across template/algorithm pairs.  For instance,
      \antsOwl and \cmtkCow have consistently smaller standard
      deviations because they are more regularized.}
\label{tab:jacdet_mnStd}
\end{table}

The mean values of the Jacobian determinant for \jfrc\,2013 appear to be
significantly different both from 1.0 and the other templates. This
could be due to a difference in size of that individual template and the
cohort of individuals that we tested with, though the affine part of the
transformation should account for gross size differences, and this
statistic is computed from the deformation only, making it difficult to
reach a definitive conclusion.

\subsection{Compare Jacobian and Hessian measures}
\label{sec:defMeasuresCompare}

In Section~4.2.2, we used the standard deviation of the
Jacobian determinant over space to measure deformation. One reason for
this is that the registration libraries we tested here (\ants, \cmtk,
\elastix) provide functions to compute that quantity.  
In the sections above, we explained why the standard deviation of the
distribution of of the Jacobian determinant (\jsd) is a sensible measure
of deformation. To reiterate, \jsd quantifies the extent to which 
a transformation \emph{simultaneously} stretches and shrinks space.

Despite its prevalent use, it may not always measure the kind of
deformation researchers care to avoid, because there are transformations
with large values of \jsd that may not reflect deformation in the
intuitive sense.  In Section~4.2.3, we describe how another 
measure, the Hessian matrix Frobenius norm mean (\hfm), could be useful.
Here we compare the \hfm to the \jsd.


\begin{figure}
  \includegraphics[width=\textwidth]{Hessian_vs_Jacobian_ed_20190820.pdf}
  \caption{Scatterplot showing the standard deviation of the Jacobian
    determinant (\jsd) and the mean of the Hessian's Frobenius norm for
    template-algorithm pairs.}
  \label{fig:HfmVsJsd}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{distWarpNorm_vs_Hessian_ed_20190820.pdf}
  \caption{Scatterplot showing mean skeleton distance and the mean 
  of the Hessian's Frobenius norm for template-algorithm pairs.}
  \label{fig:distVsHessian}
\end{figure}

\Cref{fig:HfmVsJsd} plots \jsd against \hfm.  As we would expect, these
measure are positively correlated (correlation coefficient $0.63$), but we observe some systematic
patterns. Most interestingly, transformations estimated with \ants tend
to be less smooth when measured by \hfm than transformations from either
\elastix or \cmtk, even when the measures of \jsd are the same or
similar.  This could be because the \ants parameter sets we tested
generally produce diffeomorphisms (\antsDog, \antsYang), whereas
\elastix and \cmtk parametrize the transformations using splines.

Making a concrete recommendation between \jsd and \hfm is difficult
without a careful, dedicated study. The relationship 
between these measures and the qualitative ``distortion'' we wish to avoid 
varies by algorithms and templates.  \jsd seems to capture
larger-scale distortions that are easier to assess visually. \hfm
appears to capture smaller scale spatial distortions.  As a result,
there are examples with higher \hfm that visually appear less distorted.
This could be because some ``correct'' or good transformations can have
small scale distortions.
As an example, comparing the algorithms \ants~A and \jrc~2018 \cmtk~A
for the template \jrc~2018 (Figures 15 and 18 in
Supplementary Notes C), we see that both have similar values of
\jsd, but \ants~A has a larger \hfm than \cmtk~A (\Cref{fig:HfmVsJsd}).
Yet, \hfm is anti-correlated to our expectations, and \jsd is
insensitive to it.

To summarize, \jsd and \hfm measure different but correlated properties of a
transformation. All else being equal, we prefer transformations
where both values are small.  Given a tradeoff between \jsd and \hfm,
it seems that opting for small \jsd values yield transforms that are
``less bad.'' In some cases, \hfm can indicate which transform is 
preferable when two have similar values of \jsd.

\Cref{fig:distVsHessian} is similar to Fig. 5,
but plots the mean skeleton distance (our proxy for registration accuracy)
against the Hessian based smoothness measure (\hfm) rather than the
Jacobian based measure (\jsd).

\subsection{Inter-modality registration}
\label{sec:lmemsupp}


Zheng et al.~\cite{Zheng2017} present a complete electron microscopy
(EM) volume of the \dros brain (FAFB), including a manual registration between the EM
volume and a LM brain template. The transformation described therein
used a Fiji \cite{Schindelin2012} plugin called ELM
(\url{https://github.com/saalfeldlab/elm}) based on BigWarp
\cite{Bogovic2016} to manually place landmark
point correspondences between \fafb and \jfrc\,2013. While this approach 
can yield good results, it is limited in the following ways. Humans are
limited to exploring the 3D EM data in one (arbitrary, i.e.~not
necessarily axis-aligned) 2D cross section at a time,
and need to infer 3D structure from these views. Landmark placement can
be difficult where the primary structures are planes, lines or curves rather
than points.  Furthermore, the EM volume is very large, and it is time
consuming for a human to even look at the entire volume at a fine scale,
and therefore accuracy of the transformation is likely to vary
significantly over space. An automatically generated registration
between these two modalities would improve upon some of these
shortcomings.

\begin{figure}
    \includegraphics[width=\textwidth]{fafb_overlay_supp-preprint.pdf}
    \caption{A visualization of the registration between \jrc\,2018F (magenta)
      and the FAFB electron microscopy volume (gray).  The left
      column shows synaptic cleft predictions (magenta). 
      The right column shows the resulting registration.
      Scale bar 50\,\micron.}
  \label{fig:fafbVis}
\end{figure}

In this work, we created a new, automatic alignment between that \fafb
and the \jrc\,2018F brain template. The primary challenge is that the image contrast from EM and LM
are at vastly different scales and measure different tissue properties.
Typical approaches for inter-modality registration usually rely on
choosing a similarity measure such as mutual information that works
well across modalities, but this is highly unlikely to produce adequate results given the differences of scale
and contrast. Our approach is to use an image derived from the
EM instead of the EM image directly. Since brp-SNAP tag produces
contrast at synapses, we created an image of synapses resembling our
chemical tag stains (or nc82 images) using the automated synapse predictions
by Heinrich et al.~\cite{Heinrich2018}, applied to \fafb. The bottom row of
Fig.~3 shows that the image of synapses estimated
from EM does indeed resemble an LM template brain. 
\Cref{fig:fafbVis} shows synaptic cleft
predictions along with the registered template. We observe first that 
the locations of synapses are biologically plausible, with high density
in neuropil and low density in tracts.  Next, we see that the synaptic
clefts comprise a very small volume, reinforcing the need for blurring
before image registration to produce an image representing local
synaptic ``density'' rather than precise locations.

We reproduce the analyses by Zheng et al.~\cite{Zheng2017} that lead to
figures 5B, 5C, S7B, and S7C in that work in order to demonstrate that
our proposed brain template and this EM to LM transformation will be
useful in this domain. The conclusions drawn by Zheng et al.~\cite{Zheng2017} are
that projection neurons (PNs) arising from the same glomerulus cluster
more tightly in the calyx of the mushroom body than previous observed
using light microscopy.

One set of neurons was manually traced in \fafb 
by Zheng et al.~\cite{Zheng2017}, while the other set came from the
Fly Circuit \cite{Chiang2011} database of neurons from light microscopy, pooling
over several individual fly brains that have been co-registered to the
\fcwb template, as described by Costa et al.~\cite{Costa2016}.  The neurons traced
from LM were transformed to the space of \jfrc\,2010 using bridging
registrations from Manton et al.~\cite{Manton2014}. The neurons traced from EM went
through two transformations: the first from \fafb to \jfrc\,2013
generated manually as described above.  The second was the automatically
estimated transformation between \jfrc\,2013 and \fcwb~\cite{Manton2014}.

\begin{figure}
  \begin{tabular}{rc}
     (a) \\ 
     (b) & \includegraphics[width=0.9\textwidth]{distance_scatter-preprint.pdf} \\
     (c) &\includegraphics[width=0.9\textwidth]{distance_hist-preprint.pdf} \\
     (d) &\includegraphics[width=0.9\textwidth]{nblast_scatter-preprint.pdf} \\
         &\includegraphics[width=0.9\textwidth]{nblast_hist-preprint.pdf}
  \end{tabular}
    \caption{Scatterplots and histograms of (a)-(b) mean skeleton distance
    and (c)-(d) NBLAST scores for various projection neuron subtypes traced from
    electron microscopy.}
    \label{fig:nblast}
\end{figure}

The details of the analysis can be found in Zheng et al.~\cite{Zheng2017}, but
briefly, it consists of identifying projection neurons (PNs) in the
calyx and grouping these by the glomerulus to which they project,
then computing similarity/distance measures between pairs of PNs in the
same group.  Two related similarity scores between pairs of neurons
were computed: the NBLAST score \cite{Costa2016} and the mean Euclidean
distance (NBLAST uses distance as part of its computation of score).

We repeated this analysis using our automatically generated
transformation from \fafb to \jrc\,2018F followed by our bridging
transformation from \jrc\,2018F to \fcwb. This shows that 
our new automatic EM to LM transform can be used to draw the same
conclusions. \Cref{fig:nblast} shows the results. Red and blue points and bars
show the measurements of \fafb traced neurons and LM database neurons,
respectively, exactly as in Zheng et al.~\cite{Zheng2017} with ELM. Black points and bars
show the same results using our EM to LM transformation. Notice that the
distributions of red and black are very similar as they should be, given
that they are measures of the same neurons transformed in different
ways. Note that the LM neurons in this analysis were co-registered to
the \fcwb template \cite{Costa2016,Jefferis2007}, which we have shown
to be a less reliable/accurate registration target than \jrc\,2018F.
Better results (more similar to the EM) could be possible by
co-registering to an improved brain template. 

\subsection{Block format for transformations}
\label{sec:h5xfm}

In this section, we motivate and describe a format for storing
displacement fields in a block-based (HDF5) file format.
Details of the file specification and examples of usage can be found
online at
\url{https://github.com/saalfeldlab/template-building/wiki/Hdf5-Deformation-fields}.

Displacement fields are a common and portable representation for
deformable transformations.  The \ants library outputs its
transformation as displacement fields.  \cmtk and \elastix use a
b-spline representation, and therefore produce output in a more compact
(memory-efficient) format, but this comes at the cost of portability. \cmtk
and \elastix each use a custom format that cannot be interchanged between
software packages, and therefore both also provide tools for converting
transforms to displacement fields. 

Displacement fields are typically stored as (32-bit) float valued vector
fields, and are therefore costly to store and load from disk.  The
overhead of loading is especially costly when transforming a sparse set
of points rather than an entire image because the entire field is
typically loaded from disk before any point coordinates can be
transformed. A block-based storage scheme would incur less overhead
because it enables efficient access of subset of the displacement field
data. HDF5 (``h5'') is a popular and ubiquitous choice for such storage
needs \cite{hdf5}. 

Storage and loading overhead can be improved even further by quantizing
and/or downsampling displacement fields.  Here we show results
quantizing displacement fields to 16-bit precision reducing the raw
memory burden by a factor of two.  In practice, we observe much greater
reductions (over factor of 10 because the quantized displacement fields
can be compressed much more efficiently than the original fields, see
\Cref{tab:h5ErrorsSizes}).

Quantization inevitably will result in errors relative to the original
data, but the magnitude of these errors can be controlled. In our case,
we specify the maximum tolerable error as 0.04\,\micron (less than
one-tenth of the pixel spacing). \Cref{tab:h5ErrorsSizes} shows mean and
maximum quantization error (rows with a downsample factor of one),
and we observe that errors are less than the specified 0.04\,\micron
threshold as expected.  \Cref{tab:h5ErrorsSizes} also shows errors
incurred when both quantizing and downsampling displacement fields by
factors of two or four. Downsampling errors are not controllable, as
they depend on how quickly the transform changes over space.  We observe
a maximum error of 1.86\,\micron (about three voxel widths) for a
downsample factor of four.


\begin{table}[H]
\scriptsize
\centering
  
  \begin{tabular}{llcrrrr}
\toprule
Transform         & & Downsample  & \multicolumn{2}{c}{Error (\textmu m)} & 
    H5 File size & Compression \\
\cline{4-5}
(Forward/Inverse) &  & factor & Mean  & Max & (Mb) & factor \\
\midrule
\multirow{6}{*}{JRC2018F FAFB}
                 &     & 1 & 0.0110 & 0.0385 & 247.6 &   11.4 \\
                 & Fwd & 2 & 0.0182 & 0.4464 &  39.0 &   72.7 \\
                 &     & 4 & 0.0524 & 1.2518 &   5.7 &  496.5 \\
\cline{2-5}
\cline{6-7}
                 &     & 1 & 0.0110 & 0.0383 & 243.3 &   11.6 \\
                 & Inv & 2 & 0.0184 & 0.6138 &  38.4 &   73.7 \\
                 &     & 4 & 0.0527 & 1.8581 &   5.6 &  502.0 \\
\midrule
\multirow{6}{*}{JRC2018F FCWB}
                 &     & 1 & 0.0114 & 0.0392 & 540.2 &   14.6 \\
                 & Fwd & 2 & 0.0163 & 0.6218 &  89.4 &   88.3 \\
                 &     & 4 & 0.0434 & 1.6542 &  13.8 &  570.9 \\
\cline{2-5}
\cline{6-7}
                 &     & 1 & 0.0114 & 0.0388 & 542.3 &   14.6 \\
                 & Inv & 2 & 0.0169 & 0.4460 &  90.0 &   87.7 \\
                 &     & 4 & 0.0462 & 1.2926 &  13.8 &  570.4 \\
\midrule
\multirow{6}{*}{JRC2018F JFRC2010}
                 &     & 1 & 0.0116 & 0.0388 & 683.2 &   11.9 \\
                 & Fwd & 2 & 0.0181 & 0.4150 & 112.1 &   72.6 \\
                 &     & 4 & 0.0552 & 1.2737 &  17.1 &  474.6 \\
\cline{2-5}
\cline{6-7}
                 &     & 1 & 0.0115 & 0.0395 & 702.5 &   11.6 \\
                 & Inv & 2 & 0.0184 & 0.3407 & 114.3 &   71.2 \\
                 &     & 4 & 0.0570 & 1.0231 &  17.2 &  473.2 \\
\midrule
\multirow{6}{*}{JRC2018F JFRC2013}
                 &     & 1 & 0.0113 & 0.0394 & 585.4 &   13.7 \\
                 & Fwd & 2 & 0.0154 & 0.2915 &  98.6 &   81.1 \\
                 &     & 4 & 0.0408 & 0.9458 &  15.1 &  530.7 \\
\cline{2-5}
\cline{6-7}
                 &     & 1 & 0.0114 & 0.0395 & 580.6 &   13.8 \\
                 & Inv & 2 & 0.0158 & 0.4738 &  98.2 &   81.5 \\
                 &     & 4 & 0.0422 & 0.9680 &  15.1 &  531.0 \\
\midrule
\multirow{6}{*}{JRC2018F TEFOR}
                 &     & 1 & 0.0115 & 0.0394 & 681.1 &   11.9 \\
                 & Fwd & 2 & 0.0188 & 0.3757 & 110.7 &   73.4 \\
                 &     & 4 & 0.0574 & 1.0585 &  17.1 &  474.8 \\
\cline{2-5}
\cline{6-7}
                 &     & 1 & 0.0115 & 0.0397 & 684.5 &   11.9 \\
                 & Inv & 2 & 0.0194 & 0.5686 & 110.7 &   73.5 \\
                 &     & 4 & 0.0601 & 1.5733 &  17.1 &  475.6 \\
\bottomrule
\end{tabular}
  \caption{ Errors due to quantization and downsampling, and reduction
  in file size of h5 displacement fields. For a given transformation,
  specified by (TARGET and SOURCE), and direction (Forward or Inverse),
  we report the mean and max errors due to quantization and
  downsampling. Rows with a downsample factor of one, show errors
  due to quantization only. The two rightmost columns show the size
  of the quantized and possibly downsampled displacement fields in
  Megabytes, and the compression factor relative to the compressed
  original 32-bit float field at full resolution.
}
\label{tab:h5ErrorsSizes}
\end{table}

Our software can apply transformations to neuronal skeletons (stored as
SWC files \cite{Stockley1993}) using transformations stored in several
formats. Here we compare the wall clock time required to transform the
a skeleton with identical transformations stored either as nifti files
(the direct output of \ants) or stored in the block based format
introduced here, using a set of $166$ neurons traced in \fafb using CATMAID \cite{schneider-mizell_quantitative_2016}.
This experiment measures how much block-based loading speeds up the
transformation of sparse points.  Transformation using nifti
displacement fields required an average (standard deviation) of
28.8(1.8) seconds, whereas transforming a neuron with an h5 displacement
field took 2.5(0.4) seconds, an 11x speedup.

We also compared the speed at which images can be transformed using
displacement fields stored either as uncompressed nifti files, or
compressed, quantized h5 file. In this case, we expect any differences
in speed to result from two factors.  First, the h5 displacement fields
comprise half as many bytes (before compression), so fewer bytes will
have to be loaded when using h5 files.  However, this may be offset
somewhat by the decompression necessary when loading from the h5, that
is not necessary when loading from the nifti.  We transformed 10 images
from \jrc 2018F space to \fafb space.  Using displacement fields stored
either as nifti files took an average (standard deviation) of
343.3(54.7) seconds, whereas the same transformation using h5 took
292.2(11.5) seconds (wall clock time).

\begin{thebibliography}{10}

\bibitem{Zheng2017}
Zheng Z, Lauritzen JS, Perlman E, Robinson CG, Nichols M, Milkie D, et~al.
\newblock A Complete Electron Microscopy Volume Of The Brain Of Adult
  {D}rosophila melanogaster.
\newblock bioRxiv. 2017; p. 140905.

\bibitem{Schindelin2012}
Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T,
  et~al.
\newblock {Fiji: an open-source platform for biological-image analysis.}
\newblock Nature methods. 2012;9(7):676--82.

\bibitem{Bogovic2016}
Bogovic JA, Hanslovsky P, Wong A, Saalfeld S.
\newblock {Robust registration of calcium images by learned contrast
  synthesis}.
\newblock In: International Symposium on Biomedical Imaging; 2016. p.
  1123--1126.
  
  \bibitem{Heinrich2018}
Heinrich L, Funke J, Pape C, Nunez-Iglesias J, Saalfeld S.
\newblock Synaptic Cleft Segmentation in Non-isotropic Volume Electron
  Microscopy of the Complete Drosophila Brain.
\newblock arXiv:180502718 [csCV]. 2018;.

\bibitem{Chiang2011}
Chiang AS, Lin CY, Chuang CC, Chang HM, Hsieh CH, Yeh CW, et~al.
\newblock {Three-Dimensional Reconstruction of Brain-wide Wiring Networks in
  Drosophila at Single-Cell Resolution}.
\newblock Current Biology. 2011;21(1):1--11.

\bibitem{Costa2016}
Costa M, Manton JD, Ostrovsky AD, Prohaska S, Jefferis GSXE.
\newblock {NBLAST: Rapid, Sensitive Comparison of Neuronal Structure and
  Construction of Neuron Family Databases}.
\newblock Neuron. 2016;91(2):293--311.

\bibitem{Manton2014}
Manton JD, Ostrovsky AD, Goetz L, Costa M, Rohlfing T.
\newblock {Combining genome-scale {D}rosophila {3D} neuroanatomical data by
  bridging template brains}.
\newblock bioRxiv. 2014; p. 6353.

\bibitem{Jefferis2007}
Jefferis GSXE, Potter CJ, Chan AM, Marin EC, Rohlfing T, Maurer J Calvin~R,
  et~al.
\newblock {Comprehensive Maps of Drosophila Higher Olfactory Centers: Spatially
  Segregated Fruit and Pheromone Representation}.
\newblock Cell. 2007;128(8):1187--1203.

\bibitem{hdf5}
{The HDF Group}. Hierarchical Data Format, version 5; 1997-2019.

\bibitem{Stockley1993}
Stockley EW, Cole HM, Brown AD, Wheal HV.
\newblock A system for quantitative morphological measurement and electronic
  modelling of neurons: three-dimensional reconstruction.
\newblock Journal of Neuroscience Methods. 1993;47(1-2):39--51.

\bibitem{schneider-mizell_quantitative_2016}
Schneider-Mizell CM, Gerhard S, Longair M, Kazimiers T, Li F, Zwart MF, et~al.
\newblock Quantitative neuroanatomy for connectomics in {Drosophila}.
\newblock eLife. 2016;5:e12059.
\newblock doi:{10.7554/eLife.12059}.

\end{thebibliography}

\newpage
\section{Supplementary Notes}

%\input{labelwise_dist_stats/labelwise_dist_stats.tex}
\input{tables/labelwise_dist_stats_20190912/labelwise_dist_stats.tex}

\newpage
\section{Supplementary Notes}

\section{Qualitative registration results}
\label{sec:qual_reg_results}

Finally, we present a more thorough qualitative comparison of
registration results.  We first randomly selected two of our evaluation
images, then display three z-slices of those nc82 images after
registration to a particular template, using a particular algorithm.
This is repeated for every template-algorithm pair, with the same two
moving images.  The z-slices were chosen to show similar anatomical
structures, and therefore vary across templates.  

Arrows were placed once on each template image at similar (but not
identical) locations, and are displayed at the same location in each
warped image.  Similar anatomical structure around each arrow for both
images indicates that registration is consistent from image to image.
These anatomical structures include the: mushroom body, ellipsoid body,
lateral horn, protocerebral bridge, and the anterior optic tubercle.
Large differences in structure at the same location in the two
aligned images is an indication of poor registration accuracy, but
good apparent alignment could still result from inadequate registration.
See the discussion limitations of using manually chosen landmark points
in Section~4.2.1 and Section~5.1.

Despite the limitations of this qualitative assessment, we will briefly
point out some observations given the examples below.  
For the \cmtk registrations using \jrc~2018F, the ellipsoid body
appears overly deformed using \cmtkCOG (\Cref{fig:jrc18_cmtkA}) and
\cmtkCow (\Cref{fig:jrc18_cmtkB}), but not \cmtkHideo
(\Cref{fig:jrc18_cmtkB}).  This is consistent with the \jsd.  Some
registrations produced unusable results, with significant scaling
issues, see Tefor-\cmtk~B (\Cref{fig:Tefor cmtkB}).  Over-warping is
evident using \antsYang, for example, with \jfrc~2013
(\Cref{fig:JFRC2013_antsC}).  Surprisingly, some \elastix
transformations over-warp as well despite generally low-values of \jsd,
for example \jfrc~2010 and \elastixSFA (\Cref{fig:JFRC2010_elastixA}).
It could be that \elastix does not over-warp on average, but happened to 
produce a poor result for one of the images randomly selected here.

In general, there exists an algorithm that works well for every
template.  Overall, registration quality seems most consistent when
using the \jrc~2018 template.

\clearpage

\begin{figure}
    \includegraphics[width=\textwidth]{F-antsFlip_lo_antsRegDog_prepress.pdf}
    \caption{JRC2018 antsA}
    \label{fig:jrc18_antsA}
\end{figure}

\begin{figure}
    \includegraphics[width=\textwidth]{F-antsFlip_lo_antsRegOwl_prepress.pdf}
    \caption{JRC2018 antsB}
    \label{fig:jrc18_antsB}
\end{figure}

\begin{figure}
    \includegraphics[width=\textwidth]{F-antsFlip_lo_antsRegYang_prepress.pdf}
    \caption{JRC2018 antsC}
    \label{fig:jrc18_antsC}
\end{figure}

\begin{figure}
    \includegraphics[width=\textwidth]{F-antsFlip_lo_cmtkCOG_prepress.pdf}
    \caption{JRC2018 cmtkA}
    \label{fig:jrc18_cmtkA}
\end{figure}

\begin{figure}
    \includegraphics[width=\textwidth]{F-antsFlip_lo_cmtkHideo_prepress.pdf}
    \caption{JRC2018 cmtkB}
    \label{fig:jrc18_cmtkB}
\end{figure}

\begin{figure}
    \includegraphics[width=\textwidth]{F-antsFlip_lo_cmtkCow_prepress.pdf}
    \caption{JRC2018 cmtkC}
    \label{fig:jrc18_cmtkC}
\end{figure}

\begin{figure}
    \includegraphics[width=\textwidth]{F-antsFlip_lo_elastixSFA_prepress.pdf}
    \caption{JRC2018 elastixA}
\end{figure}

\begin{figure}
    \includegraphics[width=\textwidth]{F-antsFlip_lo_elastixBigfootA_prepress.pdf}
    \caption{JRC2018 elastixB}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{FCWB_antsRegDog_prepress.pdf}
  \caption{FCWB antsA}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{FCWB_antsRegOwl_prepress.pdf}
  \caption{FCWB antsB}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{FCWB_antsRegYang_prepress.pdf}
  \caption{FCWB antsC}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{FCWB_cmtkCOG_prepress.pdf}
  \caption{FCWB cmtkA}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{FCWB_cmtkHideo_prepress.pdf}
  \caption{FCWB cmtkB}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{FCWB_cmtkCow_prepress.pdf}
  \caption{FCWB cmtkC}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{FCWB_elastixSFA_prepress.pdf}
  \caption{FCWB elastixA}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{FCWB_elastixBigfootA_prepress.pdf}
  \caption{FCWB elastixB}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{JFRC2013_lo_antsRegDog_prepress.pdf}
  \caption{JFRC2013 antsA}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{JFRC2013_lo_antsRegOwl_prepress.pdf}
  \caption{JFRC2013 antsB}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{JFRC2013_lo_antsRegYang_prepress.pdf}
  \caption{JFRC2013 antsC}
  \label{fig:JFRC2013_antsC}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{JFRC2013_lo_cmtkCOG_prepress.pdf}
  \caption{JFRC2013 cmtkA}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{JFRC2013_lo_cmtkHideo_prepress.pdf}
  \caption{JFRC2013 cmtkB}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{JFRC2013_lo_cmtkCow_prepress.pdf}
  \caption{JFRC2013 cmtkC}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{JFRC2013_lo_elastixSFA_prepress.pdf}
  \caption{JFRC2013 elastixA}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{JFRC2013_lo_elastixBigfootA_prepress.pdf}
  \caption{JFRC2013 elastixB}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{JFRCtemplate2010_antsRegDog_prepress.pdf}
  \caption{JFRC2010 antsA}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{JFRCtemplate2010_antsRegOwl_prepress.pdf}
  \caption{JFRC2010 antsB}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{JFRCtemplate2010_antsRegYang_prepress.pdf}
  \caption{JFRC2010 antsC}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{JFRCtemplate2010_cmtkCOG_prepress.pdf}
  \caption{JFRC2010 cmtkA}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{JFRCtemplate2010_cmtkHideo_prepress.pdf}
  \caption{JFRC2010 cmtkB}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{JFRCtemplate2010_cmtkCow_prepress.pdf}
  \caption{JFRC2010 cmtkC}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{JFRCtemplate2010_elastixSFA_prepress.pdf}
  \caption{JFRC2010 elastixA}
  \label{fig:JFRC2010_elastixA}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{JFRCtemplate2010_elastixBigfootA_prepress.pdf}
  \caption{JFRC2010 elastixB}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{TeforBrain_f_antsRegDog_prepress.pdf}
  \caption{Tefor antsA}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{TeforBrain_f_antsRegOwl_prepress.pdf}
  \caption{Tefor antsB}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{TeforBrain_f_antsRegYang_prepress.pdf}
  \caption{Tefor antsC}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{TeforBrain_f_cmtkCOG_prepress.pdf}
  \caption{Tefor cmtkA}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{TeforBrain_f_cmtkHideo_prepress.pdf}
  \caption{Tefor cmtkB}
  \label{fig:Tefor cmtkB}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{TeforBrain_f_cmtkCow_prepress.pdf}
  \caption{Tefor cmtkC}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{TeforBrain_f_elastixSFA_prepress.pdf}
  \caption{Tefor elastixA}
\end{figure}

\begin{figure}
  \includegraphics[width=\textwidth]{TeforBrain_f_elastixBigfootA_prepress.pdf}
  \caption{Tefor elastixB}
\end{figure}

\end{document}
