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\begin{document}
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\title{Template for MICCAI FLARE 2023 Challenge (Please use a meaningful title. Don't use "a xxx solution to FLARE Challenge")}
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%\titlerunning{Abbreviated paper title}
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\author{First Author\inst{1}\orcidID{0000-1111-2222-3333} \and
Second Author\inst{2,3}\orcidID{1111-2222-3333-4444} \and
Third Author\inst{3}\orcidID{2222--3333-4444-5555} (please list your team members here. Each team can have at most 6 members.)
} 
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\authorrunning{First Author Name here et al.}
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\institute{Princeton University, Princeton NJ 08544, USA \and
Springer Heidelberg, Tiergartenstr. 17, 69121 Heidelberg, Germany
\and
ABC Institute, Rupert-Karls-University Heidelberg, Heidelberg, Germany\\
\email{\{corresponding author\}@edu.cn}}
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\maketitle              % typeset the header of the contribution
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\begin{abstract}
The abstract should briefly summarize the main contribution of the paper and the validation performance.
Our method achieved an average DSC score of 90.34\% and 60.23\% for the organs and lesions on the validation set and the average running time and area under GPU memory-time cure are 10s and 10000MB, respectively. 
(150--250 words)
\\
The total length of the manuscript should be at least 8 pages (don't include references). There is no limitation for the maximum number of pages. 

\keywords{First keyword  \and Second keyword \and Another keyword.}
\end{abstract}



\section{Introduction}
The introduction should have at least three parts. For each part, you can write multiple paragraphs to clarify your motivations and ideas. 

P1. Introduce the background and difficulty of this challenge


P2. Related work/state-of-the-art methods on semi-supervised/partial-label segmentation


P3. Your motivation and solution/contribution. 






\section{Method}
A detailed description of the method used and a figure should be included to show your pipeline.

%###########################
\subsection{Preprocessing}
Full description of any pre-processing strategy. Please details on the following aspects
\begin{itemize} 
 \item Data clean or statistical analysis (e.g., annotation distribution of different segmentation targets, volume distribution...)
 \item Resampling method for anisotropic data
 \item Intensity normalization method
 \item Others
\end{itemize}

\subsection{Proposed Method}
\textbf{Please provide figures to show your pipeline or network architecture.} 
Figure~\ref{fig:Network} shows a typical example of 3D nnU-Net~\cite{nnUNet}.
(note: the name is \textbf{nnU-Net} rather than nnunet, nnUNet. Similarly, please use \textbf{U-Net} rather than unet, u-net).

\begin{figure}[htbp]
\centering
\includegraphics[scale=0.35]{imgs/U-Net.png}
\caption{Network architecture (Copyright preserved. Please do not directly use this figure in your manuscript.) Please also include the network description in the figure title. So reviewers could quickly understand your idea. 
}
\label{fig:Network}
\end{figure}

Explain network architecture details.

Loss function: we use the summation between Dice loss and cross-entropy loss because compound loss functions have been proven to be robust in various medical image segmentation tasks~\cite{LossOdyssey}. 

\textbf{Please introduce your strategies to deal with the partial labels.}


\textbf{Please introduce your strategies to use the 1800 unlabeled images.} If you don't use them, please explicitly say "Unlabeled images were not used."
Please also clarify whether you used the pseudo labels generated by the FLARE22 winning algorithm~\cite{FLARE22-1st-Huang} and the best-accuracy-algorithm~\cite{FLARE22-bestDSC-Wang}.



\textbf{Please introduce your strategies to improve inference speed and reduce resource consumption} 


\subsection{Post-processing}
\textbf{Description of post-processing of the model outputs to get the final output in the training stage.}
E.g., connected component analysis, testing time augmentation


\section{Experiments}
\subsection{Dataset and evaluation measures}
The FLARE 2023 challenge is an extension of the FLARE 2021-2022~\cite{MedIA-FLARE21}\cite{FLARE22}, aiming to aim to promote the development of foundation models in abdominal disease analysis. The segmentation targets cover 13 organs and various abdominal lesions. The training dataset is curated from more than 30 medical centers under the license permission, including TCIA~\cite{TCIA}, LiTS~\cite{LiTS}, MSD~\cite{simpson2019MSD}, KiTS~\cite{KiTS,KiTSDataset}, autoPET~\cite{autoPET-Data,autoPET-MICCAI22}, TotalSegmentator~\cite{TotalSegmentator}, and AbdomenCT-1K~\cite{AbdomenCT-1K}. The training set includes 4000 abdomen CT scans where 2200 CT scans with partial labels and 1800 CT scans without labels. The validation and testing sets include 100 and 400 CT scans, respectively, which cover various abdominal cancer types, such as liver cancer, kidney cancer, pancreas cancer, colon cancer, gastric cancer, and so on. The organ annotation process used ITK-SNAP~\cite{ITKSNAP}, nnU-Net~\cite{nnUNet}, and MedSAM~\cite{MedSAM}.


The evaluation metrics encompass two accuracy measures—Dice Similarity Coefficient (DSC) and Normalized Surface Dice (NSD)—alongside two efficiency measures—running time and area under the GPU memory-time curve. These metrics collectively contribute to the ranking computation. Furthermore, the running time and GPU memory consumption are considered within tolerances of 15 seconds and 4 GB, respectively.


\subsection{Implementation details}
\subsubsection{Environment settings}
The development environments and requirements are presented in Table~\ref{table:env}.


\begin{table}[!htbp]
\caption{Development environments and requirements.}\label{table:env}
\centering
\begin{tabular}{ll}
\hline
System       & e.g., Ubuntu 18.04.5 LTS or Windows 11\\
\hline
CPU   & e.g., Intel(R) Core(TM) i9-7900X CPU@3.30GHz \\
\hline
RAM                         &16$\times $4GB; 2.67MT$/$s\\
\hline
GPU (number and type)                         & Four NVIDIA V100 16G\\
\hline
CUDA version                  & e.g., 11.0\\                          \hline
Programming language                 & e.g., Python 3.20\\ 
\hline
Deep learning framework & e.g., torch 2.0, torchvision 0.2.2 \\
\hline
Specific dependencies         &                        \\                                                                      
\hline
Code     &                                                                \\
\hline
\end{tabular}
\end{table}


\subsubsection{Training protocols}
Please describe at least the following aspects:

1. processing of the unlabeled images and partial labels

2. Data augmentation (Based on the winning solutions in FLARE 2021~\cite{MedIA-FLARE21}, we recommend using extensive data augmentation)

3. patch sampling strategy

4. optimal model selection criteria




\begin{table*}[!htbp]
\caption{Training protocols.}
\label{table:training}
\begin{center}
% \resizebox{0.47\textwidth}{!}{
\begin{tabular}{ll} 
\hline
Network initialization         & \\
\hline
Batch size                    & 2 \\
\hline 
Patch size & 80$\times$192$\times$160  \\ 
\hline
Total epochs & 1000 \\
\hline
Optimizer          &        \\ \hline
Initial learning rate (lr)  &  \\ \hline
Lr decay schedule &  \\
\hline
Training time                                           & 72.5 hours \\  \hline 
Loss function & \\     \hline
Number of model parameters    & 41.22M\footnote{https://github.com/sksq96/pytorch-summary} \\ \hline
Number of flops & 59.32G\footnote{https://github.com/facebookresearch/fvcore} \\ \hline
CO$_2$eq & 1 Kg\footnote{https://github.com/lfwa/carbontracker/} \\  \hline
\end{tabular}
%}
\end{center}
\end{table*}


\begin{table*}[!htbp]
\caption{Training protocols for the refine model (if using two-stage framework).}
\label{table:training2nd}
\begin{center}
% \resizebox{0.47\textwidth}{!}{
\begin{tabular}{ll} 
\hline
Network initialization         & \\
\hline
Batch size                    & 2 \\
\hline 
Patch size & 80$\times$192$\times$160  \\ 
\hline
Total epochs & 1000 \\
\hline
Optimizer          & SGD with nesterov momentum ($\mu=0.99$)          \\ \hline
Initial learning rate (lr)  & 0.01 \\ \hline
Lr decay schedule & halved by 200 epochs \\
\hline
Training time                                           & 72.5 hours \\  \hline 
Number of model parameters    & 41.22M\footnote{https://github.com/sksq96/pytorch-summary} \\ \hline
Number of flops & 59.32G\footnote{https://github.com/facebookresearch/fvcore} \\ \hline
CO$_2$eq & 1 Kg\footnote{https://github.com/lfwa/carbontracker/} \\  \hline
\end{tabular}
\end{center}
\end{table*}


\section{Results and discussion}
Note: Please describe at least the following aspects in this section


1. The effect of using unlabelled cases;


2. In what kind of cases the proposed method works well?

3. What are the possible reasons for the failed cases or organs?


4. Segmentation efficiency analysis


\begin{table}[htbp]
\caption{Quantitative evaluation results. \textbf{The results should correspond to your final docker submission. The public validation denotes the performance on the 50 validation cases with ground truth. Please present both the mean score and standard deviation. The online validation denotes the leaderboard results. The Testing results will be released during MICCAI. Please leave them blank at present.} You can use a similar 
Table format to present the ablation study results of the public and online validation. A useful online tool to create latex table \url{https://www.tablesgenerator.com/latex_tables.}
}\label{tab:final-results}
\centering
\begin{tabular}{l|cc|cc|cc}
\hline
\multirow{2}{*}{Target} & \multicolumn{2}{c|}{Public Validation} & \multicolumn{2}{c|}{Online Validation} & \multicolumn{2}{c}{Testing} \\ \cline{2-7} 
                        & DSC(\%)            & NSD(\%)           & DSC(\%)            & NSD(\%)           & DSC(\%)      & NSD (\%)     \\ \hline
Liver                   & 83.45 $\pm$ $5.32$                  &   83.45 $\pm$ 5.32                  &   83.45                 &     83.45                &            &              \\
Right Kidney            &                    &                   &                    &                   &              &              \\
Spleen                  &                    &                   &                    &                   &              &              \\
Pancreas                &                    &                   &                    &                   &              &              \\
Aorta                   &                    &                   &                    &                   &              &              \\
Inferior vena cava      &                    &                   &                    &                   &              &              \\
Right adrenal gland     &                    &                   &                    &                   &              &              \\
Left adrenal gland      &                    &                   &                    &                   &              &              \\
Gallbladder             &                    &                   &                    &                   &              &              \\
Esophagus               &                    &                   &                    &                   &              &              \\
Stomach                 &                    &                   &                    &                   &              &              \\
Duodenum                &                    &                   &                    &                   &              &              \\
Left kidney             &                    &                   &                    &                   &              &              \\
Tumor                   &                    &                   &                    &                   &              &              \\ \hline
Average                   &                    &                   &                    &                   &              &              \\ \hline
\end{tabular}
\end{table}

Note to Table~\ref{tab:final-results}: if you have multiple solutions, such as a faster model with lower DSC or a slower model with higher DSC, you can use a similar Table format to report the performance on the public/online validation set.


\subsection{Quantitative results on validation set}
Please report the Dice and NSD scores of organs and tumors on the validation set

Please do ablation studies to analyze the effect of unlabelled data.




\begin{table}[htbp]
\caption{Quantitative evaluation of segmentation efficiency in terms of the running them and GPU memory consumption. Total GPU denotes the area under GPU Memory-Time curve. Evaluation GPU platform: NVIDIA QUADRO RTX5000 (16G). Note: if you didn't make validation docker submissions during the challenge, you can obtain these metrics on your local GPU. Our evaluation code has been released at \url{https://github.com/JunMa11/FLARE/tree/main/FLARE23}. You could use a similar table format for ablation studies. Please don't change the case IDs. 
}
\centering
\begin{tabular}{ccccc}
\hline
Case ID & Image Size      & Running Time (s) & Max GPU (MB) & Total GPU (MB) \\ \hline
0001    & (512, 512, 55)  & e.g., 8.23       & e.g., 2453   & e.g., 10453    \\
0051    & (512, 512, 100) &                  &              &                \\
0017    & (512, 512, 150) &                  &              &                \\
0019    & (512, 512, 215) &                  &              &                \\
0099    & (512, 512, 334) &                  &              &                \\
0063    & (512, 512, 448) &                  &              &                \\
0048    & (512, 512, 499) &                  &              &                \\
0029    & (512, 512, 554) &                  &              &                \\ \hline
\end{tabular}
\end{table}


\subsection{Qualitative results on validation set}
please show at least two examples with good segmentation results and two examples with bad segmentation results in the validation set 

the ground truth of 50 validation cases has been released 
\url{https://drive.google.com/drive/folders/16Jz7nC907UOhr7hhWLUS4X9XwgkV0qBJ?usp=drive_link}

\begin{figure}[!htbp]
\centering
\includegraphics[scale=0.25]{imgs/flare23-results.png}
\caption{please show two examples with good segmentation results and two examples with bad segmentation results in the validation set. this is a demo figure. (credit to Zhang, F. et al. FLARE22.)
}
\label{fig:seg}
\end{figure}

How to draw this kind of figure?
1. Install ITKSNAP \url{http://www.itksnap.org/pmwiki/pmwiki.php}

2. Open the image and adjust the window level and width to 40 and 400 respectively

3. Load segmentation results and adjust the label opacity to 50

4. Task snapshots (ITKSNAP has this function: click on the camera button )

5. Put all these snapshots in ppt 


\subsection{Segmentation efficiency results on validation set}


\subsection{Results on final testing set}
This is a placeholder. We will send you the testing results during MICCAI (2023.10.8).


\subsection{Limitation and future work}




\section{Conclusion}
The main finding and results


\subsubsection{Acknowledgements} The authors of this paper declare that the segmentation method they implemented for participation in the FLARE 2023 challenge has not used any pre-trained models nor additional datasets other than those provided by the organizers. The proposed solution is fully automatic without any manual intervention. We thank all the data owners for making the CT scans publicly available and CodaLab~\cite{codalab} for hosting the challenge platform. 


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% Please add the following required packages to your document preamble:
% \usepackage[normalem]{ulem}
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\begin{table}[!htbp]
\caption{Checklist Table. Please fill out this checklist table in the answer column.}
\centering
\begin{tabular}{ll}
\hline
Requirements                                                                                                                    & Answer        \\ \hline
A meaningful title                                                                                                              & Yes/No        \\ \hline
The number of authors ($\leq$6)                                                                                                             & Number        \\ \hline
Author affiliations and ORCID                                                                                           & Yes/No        \\ \hline
Corresponding author email is presented                                                                                                  & Yes/No        \\ \hline
Validation scores are presented in the abstract                                                                                 & Yes/No        \\ \hline
\begin{tabular}[c]{@{}l@{}}Introduction includes at least three parts: \\ background, related work, and motivation\end{tabular} & Yes/No        \\ \hline
A pipeline/network figure is provided                                                                                           & Figure number \\ \hline
Pre-processing                                                                                                                  & Page number   \\ \hline
Strategies to use the partial label                                                                                             & Page number   \\ \hline
Strategies to use the unlabeled images.                                                                                         & Page number   \\ \hline
Strategies to improve model inference                                                                                           & Page number   \\ \hline
Post-processing                                                                                                                 & Page number   \\ \hline
Dataset and evaluation metric section is presented                                                                              & Page number   \\ \hline
Environment setting table is provided                                                                                           & Table number  \\ \hline
Training protocol table is provided                                                                                             & Table number  \\ \hline
Ablation study                                                                                                                  & Page number   \\ \hline
Efficiency evaluation results are provided                                                                                     & Table number \\ \hline
Visualized segmentation example is provided                                                                                     & Figure number \\ \hline
Limitation and future work are presented                                                                                        & Yes/No        \\ \hline
Reference format is consistent.  & Yes/No        \\ \hline

\end{tabular}
\end{table}

\end{document}
