Applied Machine Learning in Spiral Breast-CT: Can We Train a Deep Convolutional Neural Network for Automatic, Standardized and Observer Independent Classification of Breast Density?

The aim of this study was to investigate the potential of a machine learning algorithm to accurately classify parenchymal density in spiral breast-CT (BCT), using a deep convolutional neural network (dCNN). In this retrospectively designed study, 634 examinations of 317 patients were included. After image selection and preparation, 5589 images from 634 different BCT examinations were sorted by a four-level density scale, ranging from A to D, using ACR BI-RADS-like criteria. Subsequently four different dCNN models (differences in optimizer and spatial resolution) were trained (70% of data), validated (20%) and tested on a “real-world” dataset (10%). Moreover, dCNN accuracy was compared to a human readout. The overall performance of the model with lowest resolution of input data was highest, reaching an accuracy on the “real-world” dataset of 85.8%. The intra-class correlation of the dCNN and the two readers was almost perfect (0.92) and kappa values between both readers and the dCNN were substantial (0.71–0.76). Moreover, the diagnostic performance between the readers and the dCNN showed very good correspondence with an AUC of 0.89. Artificial Intelligence in the form of a dCNN can be used for standardized, observer-independent and reliable classification of parenchymal density in a BCT examination.

SCU-Net: A deep learning method for segmentation and quantification of breast arterial calcifications on mammograms

PurposeMeasurements of breast arterial calcifications (BAC) can offer a personalized, noninvasive approach to risk-stratify women for cardiovascular disease such as heart attack and stroke. We aim to detect and segment breast arterial calcifications in mammograms accurately and suggest novel measurements to quantify detected BAC for future clinical applications.MethodsTo separate BAC in mammograms, we propose a light-weight fine vessel segmentation method Simple Context U-Net (SCU-Net). Due to the large image size of mammograms, we adopt a patch-based way to train SCU-Net and obtain the final whole-image-size results by stitching patch-wise results together. To further quantify calcifications, we test five quantitative metrics to inspect the progression of BAC for subjects: Sum of Mask Probability Metric (𝒫ℳ), Sum of Mask Area Metric (𝒜ℳ), Sum of Mask Intensity Metric (𝒮ℐℳ), Sum of Mask Area with Threshold Intensity Metric (𝒯𝒜ℳX) and Sum of Mask Intensity with Threshold X Metric (𝒯 𝒮ℐℳX). Finally, we demonstrate the ability of the metrics to longitudinally measure calcifications in a group of 26 subjects and evaluate our quantification metrics compared to calcified voxels and calcium mass on breast CT for 10 subjects.ResultsOur segmentation results are compared with state-of-the-art network architectures based on recall, precision, accuracy, F1-score/Dice Score and Jaccard Index evaluation metrics and achieve corresponding values of 0.789, 0.708, 0.997, 0.729, and 0.581 for whole-image-size results. The quantification results all show >95% correlation between quantification measures on predicted masks of SCU-Net as compared to the groundtruth and measurement of calcification on breast CT. For the calcifications quantification measurement, our calcification volume (voxels) results yield R2-correlation values of 0.834, 0.843, 0.832, 0.798, and 0.800 for the 𝒫ℳ, 𝒜ℳ, 𝒮ℐℳ, 𝒯𝒜ℳ100, 𝒯 𝒮ℐℳ100 metrics, respectively; our calcium mass results yield comparable R2-correlation values of 0.866, 0.873, 0.840, 0.774, and 0.798 for the same metrics.ConclusionsSCU-Net is a simple method to accurately segment arterial calcification retrospectively on routine mammograms. Quantification of the calcifications based on this segmentation in the retrospective cohort study has sufficient sensitivity to detect the normal progression over time and should be useful for future research and clinical applications.