Deep Learning based detection of Acute Aortic Syndrome in contrast CT images

Autor:	Yellapragada, Manikanta Srikar, Xie, Yiting, Graf, Benedikt, Richmond, David, Krishnan, Arun, Sitek, Arkadiusz
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2004.01648 Zobrazit plný text záznamu View this record from Arxiv