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

Manikanta Srikar Yellapragada; Yiting Xie; Benedikt Graf; David Richmond; Arun Krishnan; Arkadiusz Sitek

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

Computer Vision and Pattern Recognition 2020-04-06 v1 Machine Learning Image and Video Processing

Authors: Manikanta Srikar Yellapragada , Yiting Xie , Benedikt Graf , David Richmond , Arun Krishnan , Arkadiusz Sitek

Abstract

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.

Keywords

acute aortic syndrome deep learning computed tomography 3D convolutional neural network multiple instance learning medical image classification

Cite

@article{arxiv.2004.01648,
  title  = {Deep Learning based detection of Acute Aortic Syndrome in contrast CT images},
  author = {Manikanta Srikar Yellapragada and Yiting Xie and Benedikt Graf and David Richmond and Arun Krishnan and Arkadiusz Sitek},
  journal= {arXiv preprint arXiv:2004.01648},
  year   = {2020}
}

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

Abstract

Keywords

Cite

Related papers