# Computer Aided Detection of Pulmonary Embolism Using Multi-Slice Multi-Axial Segmentation
## 4 Discussion  
CAD methods for pulmonary embolism have long suffered from a moderate sensitivity at high regimes of false positives per scan, hindering their clinical use. Recent neural network methods have improved the results of CAD for PE, using the same classic machine learning methodology consisting of finding candidate points and then evaluating whether each candidate belongs to an embolus. We present a method that avoids the use of candidate points by treating CAD for PE as a segmentation problem, turning the segmentation into detection points as a post-processing step. We evaluated three methods with this underlying idea: a method using axial slices as input data, a method using a slab of the CT scan, and a method that combines information from axial, sagittal, and coronal planes. The three methods presented outperformed the other participants on the CAD-PE challenge. This may be because there was information about the location of the embolus that was relevant to its shape and image characteristics. A distal embolus might not look similar if it is in the upper lobes or the lower lung lobes. By using global segmentation strategies, the network can learn such differences. When compared with the work of [PERSON] et al. [17], the proposed methods showed lower performance at the regime of two false positives per scan. It should be noted that the performance of their method at lower FPS regimes, which are required for clinical practice, was unclear. The methods improved when more data were added as input to the network. As such, the \(2.5D\) network outperformed the \(2D\) network on all regimes of FPS. This is logical. Emboli are \(3D\) structures, and image variations on the \(z\)-axis contain valuable information. The authors were surprised to see that the \(3D\) method did not clearly outperform the \(2.5D\) method on all FPS regimes. However, there was a clear improvement in performance at very low FPS regimes ([0-0.25]), which indicates that the \(3D\) method successfully increases the score of certain emboli. This effect makes sense, since some emboli that might not be clear in axial slices may be very clear in coronal or sagittal slices, and the context on such planes might be more relevant than the few slices that the \(2.5D\) method uses. Surprisingly, the performance in terms of segmentation decreased with complexity, with the \(2D\) method achieving a  
Figure 5: Histograms of emboli volumes shown in green with detected emboli shown in blue for the three proposed methods: (**Left**) the \(2D\) network; (**Middle**) the \(2.5D\) network; and (**Right**) the \(3D\) network. The more complex is the network, the more consistently it detected emboli. All methods found it difficult to detect small emboli (less than 0.5 mL). better Dice coefficient than the \(2.5D\) or \(3D\) method. This might indicate that the \(3D\) network is more restrictive in its findings, producing fewer false positives while keeping the embolus located. There are several limitations to this study. First, we used only 60 CT scans for training. While they account for more than 7000 slices, this number is small for training modern neural networks and could be considered a small dataset. A larger number of scans would likely improve the performance of the network. A thorough study on the improvement of performance with respect to the number of training scans might shed more light on the optimal number of scans required for training. Second, the scans on which we trained the dataset and the scans on which we tested them come from the same institution and had been obtained with the same acquisition machines, which might bias the results positively. The evaluation on external datasets, such as the one in [22], was left for future work. Third, there is room to improve the segmentation performance of the network, since the baseline network structure is very simple. More complex networks, using squeeze and excite blocks, for instance, may greatly improve segmentation results that could lead to better emboli detection.