Automated analysis and detection of abnormalities in transaxial anatomical cardiovascular magnetic resonance images: a proof of concept study with potential to optimize image acquisition

Abstract

The large number of available MRI sequences means patients cannot realistically undergo them all, so the range of sequences to be acquired during a scan are protocolled based on clinical details. Adapting this to unexpected findings identified early on in the scan requires experience and vigilance. We investigated whether deep learning of the images acquired in the first few minutes of a scan could provide an automated early alert of abnormal features. Anatomy sequences from 375 CMR scans were used as a training set. From these, we annotated 1500 individual slices and used these to train a convolutional neural network to perform automatic segmentation of the cardiac chambers, great vessels and any pleural effusions. 200 scans were used as a testing set. The system then assembled a 3D model of the thorax from which it made clinical measurements to identify important abnormalities. The system was successful in segmenting the anatomy slices (Dice 0.910) and identified multiple features which may guide further image acquisition. Diagnostic accuracy was 90.5% and 85.5% for left and right ventricular dilatation, 85% for left ventricular hypertrophy and 94.4% for ascending aorta dilatation. The area under ROC curve for diagnosing pleural effusions was 0.91. We present proof-of-concept that a neural network can segment and derive accurate clinical measurements from a 3D model of the thorax made from transaxial anatomy images acquired in the first few minutes of a scan. This early information could lead to dynamic adaptive scanning protocols, and by focusing scanner time appropriately and prioritizing cases for supervision and early reporting, improve patient experience and efficiency.

The impact of artificial intelligence on clinical education: perceptions of postgraduate trainee doctors in London (UK) and recommendations for trainers

“…Natural language processing can automate clinical documentation by text summarization [ 11 ] and detect diagnoses from scan reports [ 12 ]. Computer vision algorithms can detect lesions from radiological scans, make measurements, and pick-up incidental findings, saving time and reducing error [ 13 , 14 ]. Robots trained by human operators could perform procedures such as venepuncture, and ultrasound [ 15 , 16 ].…”

Quality assurance of late gadolinium enhancement cardiac magnetic resonance images: a deep learning classifier for confidence in the presence or absence of abnormality with potential to prompt real-time image optimization

“…Previous work has shown that deep learning can be used to automatically plan CMR standard view images [20] . Deep learning algorithms can also be implemented to analyze CMR images while the scan is ongoing, and their output is useful to guide selection of scan protocols based on initial findings [21] . However, leveraging AI technology for real-time CMR quality assurance has so far been limited to tissue segmentation and automatic measurements rather than assessing LGE likelihood [22] , [23] .…”

PAT-CNN: Automatic Segmentation and Quantification of Pericardial Adipose Tissue from T2-Weighted Cardiac Magnetic Resonance Images

“…Preprocessing Ground truth PAT segmentation was semi-automatically performed in 70 training images (Fig 1). As the first step, we identified the cardiac chambers and left ventricular (LV) myocardium using HRNet, an existing CNN trained in T2-weighted images with a similar contrast [11]. Using these segmentations, we created a bounding box around the heart.…”

“…Aims In this project, we aim to estimate PATV from clinical CMRI. We focus on axial T2-weighted rapid gradient-echo scans which are routinely acquired in the clinic and in which CNNs have been used to accurately segment several cardiac structures [11]. We initially create a non-learned semi-automatic image processing protocol to segment PAT and generate training data for the proposed CNN.…”