# Computer Aided Detection of Pulmonary Embolism Using Multi-Slice Multi-Axial Segmentation
###### Abstract  
Pulmonary Embolism (PE) is a respiratory disease caused by blood clots lodged in the pulmonary arteries, blocking perfusion, limiting blood oxygenation, and inducing a higher load on the right ventricle. Pulmonary embolism is diagnosed using contrast enhanced Computed Tomography Pulmonary Angiography (CTPA), resulting in a \(3D\) image where the pulmonary arteries appear as bright structures, and emboli appear as filling defects, with these often being difficult to see, especially in the subsegmental case. In comparison to an expert panel, the average radiologist has a sensitivity of between 77% and 94%. Computer Aided Detection (CAD) is regarded as a promising system to detect emboli, but current algorithms are hindered by a high false positive rate. In this paper, we propose a novel methodology for emboli detection. Instead of finding candidate points and characterizing them, we find emboli directly on the whole image slice. Detections across different slices are merged into a single detection volume that is post-processed to generate emboli detections. The system was evaluated on a public PE database of 80 scans. On 20 test scans, our system obtained a per-embolus sensitivity of 68% at a regime of one false positive per scan, improving on state-of-the-art methods. We therefore conclude that our multi-slice emboli segmentation CAD for PE method is a valuable alternative to the standard methods of candidate point selection and classification.  
pulmonary embolism; computed aided detection; computed tomography; segmentation; convolutional neural networks +
Footnote †: journal: Medical  
Pulmonary Angiography (CTPA) being the gold standard. This procedure involves injecting a contrast agent into the patient's pulmonary arteries, imaging with a computed tomography (CT) scanner and reading the images to find filling defects. Such filling defects are the result of the emboli blockage of the contrast agent. Since the clinical symptoms for PE are non-specific and there is no laboratory test for the diagnosis of PE, many of the CTPA performed in clinical settings are not positive for PE.  
Computer Aided Detection (CAD) methods have been used in PE diagnosis to measure the right ventricle strain [4; 5] and to detect PE automatically. In comparison with a panel of expert radiologists, the sensitivity of an individual radiologist ranges from 77% to 94% [6; 7]. Works on using CAD systems as a second reader have shown increases in reader sensitivity of 92-98% [6; 7; 8; 9; 10; 11; 12; 13]. However, current systems of CAD for PE show moderate sensitivity (65%) at a fairly high false positive rate (three false positives per study), preventing their general adoption in clinical practice [14].  
CAD algorithms for PE follow a two-step process, in which a set of candidate points are selected and tested to discriminate between emboli and false detections. Older studies extracted hand-crafted features around candidate points and used classification techniques to evaluate them [9; 12]. Modern methods take advantage of convolutional neural networks for both feature extraction and classification by generating planar reformatted images centered around candidate points [15; 16]. The more recent work [17] also includes context information around the candidate point to improve detection performance.  
In this work, we hypothesize that deep convolutional neural networks can be used to directly detect emboli from images, without the need to detect candidate points. By using the whole image to detect emboli, we let the network use both local and global image characteristics to discern between PE and other image structures.