Deep Learning based detection of Acute Aortic Syndrome in contrast CT images
This work addresses a critical medical diagnosis problem for patients with life-threatening aortic conditions, but it is incremental as it applies existing deep learning methods to a specific domain.
The researchers tackled the detection of Acute Aortic Syndrome (AAS) in contrast CT images by developing an end-to-end automatic approach using 3D CNN and MIL classifiers, achieving AUCs of 0.965 and 0.985 on a test set of 280 volumes.
Acute aortic syndrome (AAS) is a group of life threatening conditions of the aorta. We have developed an end-to-end automatic approach to detect AAS in computed tomography (CT) images. Our approach consists of two steps. At first, we extract N cross sections along the segmented aorta centerline for each CT scan. These cross sections are stacked together to form a new volume which is then classified using two different classifiers, a 3D convolutional neural network (3D CNN) and a multiple instance learning (MIL). We trained, validated, and compared two models on 2291 contrast CT volumes. We tested on a set aside cohort of 230 normal and 50 positive CT volumes. Our models detected AAS with an Area under Receiver Operating Characteristic curve (AUC) of 0.965 and 0.985 using 3DCNN and MIL, respectively.