# Needle Segmentation in Volumetric Optical Coherence Tomography Images for Ophthalmic Microsurgery
## 4 Discussion  
This study modified and improved two methods based on the previous work to tackle the challenges for needle segmentation in OCT images: the MF method and the FCN method. The MF based method mainly relies on the needle shadow feature which is hand-crafted by researchers. The FCN method learns the features by itself which is more efficient and less time-consuming as it avoids the cumbersome hand designing phase. The evaluation of two methods is performed with 60 OCT cubes using 7680 B-scan images captured on ex-vivo pig eyes. The experimental results show that FCN method has a better segmentation performance in terms of four evaluation metrics than the MF method. Specifically, the overall average FN needle pixel number is 187.2, and 34.1; the average needle pixel accuracy rate is 90.0%, and 94.7%; the average bounding box accuracy rate is 92.5%, and 97.6% and average pixels error for bounding box is 0.09, and 0.07; for the MF method and FCN method, respectively. Although this study is not targeting on obtaining the highest performance by tunning the FCN network, Our results elucidate that deep learning method indeed generates a powerful model on  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stage** & **Mean (ms)** & **Variance (ms)** \\ \hline Loading & 0.71 & 0.10 \\ Filtering & 7.87 & 2.22 \\ Detection & 17.02 & 20.55 \\ Total & 25.6 & 22.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: MF method inference time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stage** & **Mean (ms)** & **Variance (ms)** \\ \hline Loading & 0.72 & 0.11 \\ CNN & 97.46 & 4.67 \\ Fusion & 6.63 & 0.38 \\ Total & 121.83 & 4.91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: FCN method inference time.  
Figure 6: The accuracy performance of FCN and MF based methods. (**a**) Comparison of false negative (FN) and false positive (FP) for the average number needle pixels using two methods; (**b**) Comparison of average accuracy rate of needle pixels using two methods; (**c**) Comparison of average absence rate of bounding box using two methods; (**d**) Comparison of average pixels error for width of bounding box using two methods.  
our small dataset. A future direction is indicated to try different deep learning methods and record more training data. Regarding the runtime, the average inference time of the MF method is four times shorter than FCN method, that is 25.6 ms, and 121.83 ms, respectively. The MF method is a good choice when real-time information is required. The inference time of FCN method can also be improved using parallel programme on more advanced GPU platform. It is possible to get a balance between efficiency and complexity by fusing these two methods together. It is also worth to note that the variance of total inference time for FCN method is lower than MF method for the number of operations in FCN remaining the same in different input images.  
Although both methods achieve an average needle pixel accuracy rate above 90.0%, they have problems on segmenting the needle tips, especially for the very beginning part. A potential reason is that the tiny needle segments in B-scan, the image noise as well as the shadows caused by light diffraction principle can be easily mixed with each other. There is a clear improvement from FCN method on the performance for the needle body part, showing almost perfect segmentation results. This also indicates that our future work should focus on improving the needle segmentation performance on the tiny needle tips.