Investigation of clinical target volume segmentation for whole breast irradiation using three-dimensional convolutional neural networks with gradient-weighted class activation mapping

• Published in: Radiological physics and technology Vol. 14; no. 3; pp. 238 - 247
• Main Authors: Oya, Megumi, Sugimoto, Satoru, Sasai, Keisuke, Yokoyama, Kazuhito
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.09.2021; Springer Nature B.V
• Subjects: Artificial neural networks; Breast cancer; Datasets; Decision making; Imaging; Irradiation; Mapping; Medical and Radiation Physics; Medicine; Medicine & Public Health; Neural networks; Nuclear Medicine; Radiology; Radiotherapy; Segmentation; Target recognition; Deep learning; Whole breast irradiation; Segmentation; 3D-UNet; Grad-CAM
• ISSN: 1865-0333; 1865-0341
• DOI: 10.1007/s12194-021-00620-8

This study aims to implement three-dimensional convolutional neural networks (3D-CNN) for clinical target volume (CTV) segmentation for whole breast irradiation and investigate the focus of 3D-CNNs during decision-making using gradient-weighted class activation mapping (Grad-CAM). A 3D-UNet CNN was adopted to conduct automatic segmentation of the CTV for breast cancer. The 3D-UNet was trained using three datasets of left-, right-, and both left- and right-sided breast cancer patients. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC). Grad-CAM was applied to trained CNNs. The DSCs for the datasets of the left-, right-, and both left- and right-sided breasts were on an average 0.88, 0.89, and 0.85, respectively. The Grad-CAM heatmaps showed that the 3D-UNet used for segmentation determined the CTV region from the target-side breast tissue and by referring to the opposite-side breast. Although the size of the dataset was limited, DSC ≥ 0.85 was achieved for the segmentation of breast CTV using the 3D-UNet. Grad-CAM indicates the applicable scope and limitations of using a CNN by indicating the focus of such networks during decision-making.