Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network

• Published in: Journal of digital imaging 2024-06, Vol.37 (3), p.1067-1085
• Main Authors: Chowa, Sadia Sultana, Azam, Sami, Montaha, Sidratul, Bhuiyan, Md Rahad Islam, Jonkman, Mirjam
• Format: Article
• Language: English
• Subjects: Ablation; Accuracy; Algorithms; Artificial neural networks; Breast cancer; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - pathology; Breast Neoplasms - surgery; Classification; Correlation coefficient; Correlation coefficients; Diagnostic systems; Female; Graph theory; Humans; Image classification; Image Interpretation, Computer-Assisted - methods; Image processing; Image reconstruction; Imaging; Imaging, Three-Dimensional - methods; Machine learning; Medical imaging; Medicine; Medicine & Public Health; Neural networks; Neural Networks, Computer; Nodes; Original Paper; Pattern analysis; Radiology; Tumors; Ultrasonic imaging; Ultrasonography, Mammary - methods; Ultrasound
• ISSN: 2948-2933; 0897-1889; 2948-2925; 2948-2933; 1618-727X
• DOI: 10.1007/s10278-024-00983-5

This study proposes a novel approach for breast tumor classification from ultrasound images into benign and malignant by converting the region of interest (ROI) of a 2D ultrasound image into a 3D representation using the point-e system, allowing for in-depth analysis of underlying characteristics. Instead of relying solely on 2D imaging features, this method extracts 3D mesh features that describe tumor patterns more precisely. Ten informative and medically relevant mesh features are extracted and assessed with two feature selection techniques. Additionally, a feature pattern analysis has been conducted to determine the feature’s significance. A feature table with dimensions of 445 × 12 is generated and a graph is constructed, considering the rows as nodes and the relationships among the nodes as edges. The Spearman correlation coefficient method is employed to identify edges between the strongly connected nodes (with a correlation score greater than or equal to 0.7), resulting in a graph containing 56,054 edges and 445 nodes. A graph attention network (GAT) is proposed for the classification task and the model is optimized with an ablation study, resulting in the highest accuracy of 99.34%. The performance of the proposed model is compared with ten machine learning (ML) models and one-dimensional convolutional neural network where the test accuracy of these models ranges from 73 to 91%. Our novel 3D mesh-based approach, coupled with the GAT, yields promising performance for breast tumor classification, outperforming traditional models, and has the potential to reduce time and effort of radiologists providing a reliable diagnostic system.