# Needle Segmentation in Volumetric Optical Coherence Tomography Images for Ophthalmic Microsurgery
## 3 Experiments and Results
### Results  
Among 60 OCT cubes, 40 OCT cubes with 5120 images are applied to training the proposed network, while the rest 20 OCT cubes are used to verify the CNN network meanwhile giving a comparison with the MF based method. The needle in each cube has different pose and position, but all B-scan image sequences follow a pattern of no needle appearance to needle appearance since the needle appearance is always continuous. In order to make the segmentation result of each cube comparable, we mapped all indexes of B-scan images in one cube into three piecewise intervals with the increasing index, (1) no needle above the tissue, (2) needle appearance, and (3) needle above the tissue but out of OCT image range thus reflection exists. All indexes of B-scan images were mapped such that 0-25% is the first interval, 25-75% is the second interval and 75-100% is the third interval. Therefore, in case in an OCT cube the index for the first needle appearance image (taken from the ground truth) is at 50 and the index for needle end image is at 100, the currently evaluated image has an index of 60, and its metrics information will be at \(25\%+(60-50)/(100-50)=45\%\). The metrics for this image are then sorted into buckets of 2% and the values are averaged over other images' metrics value in the given bucket.  
The evaluation of two methods under four metrics is shown in Figure 6. Figure 6a shows the comparison of FN and FP for the average needle pixel number using two methods. The average pixel number of FP for MF and FCN is almost equal to 0 which means that few background pixels are classified as needle points. For the performance of average pixel number for FN, both methods have problems with recognition at the beginning of the needle. This artifact is probably caused by unclear shadow of the small needle tip segmentation. However, the FCN performs better than the MF which indicates fewer needle pixels are incorrectly classified to the background. The overall average FN pixel number for two methods are 187.2 and 34.1, respectively. Especially, the FCN can almost segment all of the needle pixels for the needle body part. Figure 6b shows the accuracy rate of needle pixel for two methods, which further indicates that the beginning of needle tip part has a low accuracy rate with 0.19 and 0.33 for MF and FCN, respectively. Both methods give an acceptable accuracy rate during the needle body, while FCN has a better accuracy rate. The comparison of detection accuracy rate for bounding box is shown in Figure 6c. The rate of missing bounding boxes for MF method is quite dramatic in the beginning with around 80%, but rapidly drops to a steady 10% until around the middle. Starting from the middle, almost all bounding boxes are calculated correctly. The FCN method has a similar pattern with better results. It has 66.6% of missing bounding box rate at most for the beginning of needle tip and almost none of missing bounding box detection for the needle body part. Figure 6d gives the average width error of bounding box detection in the same level that the maximum of average width error is under 1 pixel.  
We also analyze the performance results of two methods with each processing stage per B-scan image (see Tables 1 and 2). The FCN method has a 121.83 ms average inference time while the MF method has a shorter inference time of 25.6 ms, indicating that MF method performs better for time sensitive purpose usage on the current platform. The variance of FCN method is lower than MF method as the number of operations is always the same in FCN method, revealing that the FCN method is more robust to the dataset and more general to the application.