LOCALIZING IMAGE-BASED BIOMARKER REGRESSION WITHOUT TRAINING MASKS: A NEW APPROACH TO BIOMARKER DISCOVERY.

Biomarker inference from biomedical images is one of the main tasks of medical image analysis. Standard techniques follow a segmentation-and-measure strategy, where the structure is first segmented and then the measurement is performed. Recent work has shown that such strategy could be replaced by a direct regression of the biomarker value in using regression networks.

Biomarker Localization From Deep Learning Regression Networks.

Biomarker estimation methods from medical images have traditionally followed a segment-and-measure strategy. Deep-learning regression networks have changed such a paradigm, enabling the direct estimation of biomarkers in databases where segmentation masks are not present. While such methods achieve high performance, they operate as a black-box.

On the Relevance of the Loss Function in the Agatston Score Regression from Non-ECG Gated CT Scans.

In this work, we evaluate the relevance of the choice of loss function in the regression of the Agatston score from 3D heart volumes obtained from non-contrast non-ECG gated chest computed tomography scans. The Agatston score is a well-established metric of cardiovascular disease, where an index of coronary artery disease (CAD) is computed by segmenting the calcifications of the arteries and multiplying each calcification by a factor related to their intensity and their volume, creating a final aggregated index. Recent work has automated such task with deep learning techniques, even skipping the segmentation step and performing a direct regression of the Agatston score.

Automated Agatston Score Computation in non-ECG Gated CT Scans Using Deep Learning.

Introduction: The Agatston score is a well-established metric of cardiovascular disease related to clinical outcomes. It is computed from CT scans by a) measuring the volume and intensity of the atherosclerotic plaques and b) aggregating such information in an index.

Objective: To generate a convolutional neural network that inputs a non-contrast chest CT scan and outputs the Agatston score associated with it directly, without a prior segmentation of Coronary Artery Calcifications (CAC).