## 4 Discussion and Conclusions  
In this work, we launched a pilot study to explore the changing pattern of knee cartilage using ANN and two different quantification methods. The manual segmentation method takes more time to quantify a 3D knee MR sequence while the CDI is much faster because it selects only a few informative locations rather than measures the whole cartilage. Using the Imorphics dataset with 81 knees, we compared the prediction accuracy of cartilage change using the two quantification methods and found that the CDI generated better performance in predicting the change of cartilage thickness in a 12-month period.  
There are two interesting findings in our study: (1) The central horizontal line generated the best prediction performance compared to the other CDI locations, which indicated that the central region may have clearer and stronger changing patterns than other regions. This reflects the fact that central cartilage regions usually bear more body weight. Therefore, more attention should be paid to the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Quantification Method** & **Precision** & **Recall** & **F-Measure** & **MCC** & **AUC** \\ \hline \multirow{3}{*}{CDI} & \multirow{2}{*}{ANN} & 0.833 & 0.116 & 0.204 & 0.268 & **0.720** \\ \cline{3-6}  & & ZeroR & 0.0 & 0.0 & 0.0 & 0.500 \\ \hline \multirow{3}{*}{Manual Segmentation} & \multirow{2}{*}{ANN} & 0.481 & 0.296 & 0.367 & 0.202 & **0.656** \\ \cline{3-6}  & & ZeroR & 0.0 & 0.0 & 0.0 & 0.500 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of the performance of ANN with ZeroR using the whole medial tibia.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Quantification Method** & **Precision** & **Recall** & **F-Measure** & **MCC** & **AUC** \\ \hline \multirow{3}{*}{CDI} & Bottom with rebalance & 0.400 & 0.267 & 0.320 & 0.190 & 0.669 \\ \cline{2-6}  & Middle without rebalance & 0.500 & 0.111 & 0.182 & 0.192 & **0.766** \\ \cline{2-6}  & Top with rebalance & 0.382 & 0.684 & 0.491 & 0.254 & 0.694 \\ \cline{2-6}  & All with rebalance & 0.833 & 0.116 & 0.204 & 0.268 & 0.720 \\ \hline \multirow{3}{*}{Manual Segmentation} & X1 with rebalance & 0.190 & 0.432 & 0.264 & 0.010 & 0.521 \\ \cline{2-6}  & X2 with rebalance & 0.257 & 0.444 & 0.325 & 0.116 & 0.570 \\ \cline{1-1} \cline{2-6}  & X3 without rebalance & 0.186 & 0.178 & 0.182 & \(-\)0.029 & 0  
central region when selecting more CDI points. (2) In terms of predicting cartilage thickness change, the easier and faster quantification method CDI generated higher accuracy than manual segmentation (0.720 vs. 0.656 for all slides). The accuracy is not high for both methods and there is still space for improvement.  
One of our future work is to extend the current study to other knee compartments, including the lateral tibia, femur, and patella. Trying different machine learning methods (other than ANN) is another interesting direction. In addition, we will extend our dataset to incorporate more cases from the OAI database. This will help explore more useful information from the data and obtain a better understanding of knee OA disease.  
Conceptualization, M.Z.; Data curation, R.A.; Investigation, R.A.; Methodology, R.A.; Project administration, J.S.; Software, R.A.; Supervision, J.S. and M.Z.; Validation, Y.D.; Writing--original draft, R.A.; Writing--review & editing, R.A., J.S., Y.D. and M.Z.  
This research was funded by the National Science Foundation, grant numbers NSF-1723420 and NSF-1723429.  
The authors declare no conflict of interest.