3-D Res-CapsNet convolutional neural network on automated breast ultrasound tumor diagnosis
Journal
European Journal of Radiology
Journal Volume
138
Date Issued
2021
Author(s)
Abstract
Purpose: We propose a 3-D tumor computer-aided diagnosis (CADx) system with U-net and a residual-capsule neural network (Res-CapsNet) for ABUS images and provide a reference for early tumor diagnosis, especially non-mass lesions. Methods: A total of 396 patients with 444 tumors (226 malignant and 218 benign) were retrospectively enrolled from Sun Yat-sen University Cancer Center. In our CADx, preprocessing was performed first to crop and resize the tumor volumes of interest (VOIs). Then, a 3-D U-net and postprocessing were applied to the VOIs to obtain tumor masks. Finally, a 3-D Res-CapsNet classification model was executed with the VOIs and the corresponding masks to diagnose the tumors. Finally, the diagnostic performance, including accuracy, sensitivity, specificity, and area under the curve (AUC), was compared with other classification models and among three readers with different years of experience in ABUS review. Results: For all tumors, the accuracy, sensitivity, specificity, and AUC of the proposed CADx were 84.9 %, 87.2 %, 82.6 %, and 0.9122, respectively, outperforming other models and junior reader. Next, the tumors were subdivided into mass and non-mass tumors to validate the system performance. For mass tumors, our CADx achieved an accuracy, sensitivity, specificity, and AUC of 85.2 %, 88.2 %, 82.3 %, and 0.9147, respectively, which was higher than that of other models and junior reader. For non-mass tumors, our CADx achieved an accuracy, sensitivity, specificity, and AUC of 81.6 %, 78.3 %, 86.7 %, and 0.8654, respectively, outperforming the two readers. Conclusion: The proposed CADx with 3-D U-net and 3-D Res-CapsNet models has the potential to reduce misdiagnosis, especially for non-mass lesions. ? 2021 Elsevier B.V.
Subjects
adult; aged; Article; automation; breast cancer; computer assisted diagnosis; controlled study; convolutional neural network; diagnostic accuracy; diagnostic error; diagnostic test accuracy study; early cancer diagnosis; echomammography; female; human; image processing; image segmentation; major clinical study; middle aged; predictive value; priority journal; receiver operating characteristic; retrospective study; sensitivity and specificity; three-dimensional imaging; tumor classification; tumor volume; breast tumor; computer assisted diagnosis; diagnostic imaging; echography; Breast Neoplasms; Humans; Image Interpretation, Computer-Assisted; Neural Networks, Computer; Retrospective Studies; Ultrasonography
SDGs
Other Subjects
adult; aged; Article; automation; breast cancer; computer assisted diagnosis; controlled study; convolutional neural network; diagnostic accuracy; diagnostic error; diagnostic test accuracy study; early cancer diagnosis; echomammography; female; human; im
Type
journal article