A Full-Image Deep Segmenter for CT Images in Breast Cancer Radiotherapy Treatment

• Main Authors: Jan Schreier, Francesca Attanasi, Hannu Laaksonen
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2019-07-01
• Series: Frontiers in Oncology
• Subjects: machine learning; segmentation; breast; neural network; radiation therapy
• Online Access: https://www.frontiersin.org/article/10.3389/fonc.2019.00677/full

Radiation therapy is one of the key cancer treatment options. To avoid adverse effects in the healthy tissue, the treatment plan needs to be based on accurate anatomical models of the patient. In this work, an automatic segmentation solution for both female breasts and the heart is constructed using deep learning. Our newly developed deep neural networks perform better than the current state-of-the-art neural networks while improving inference speed by an order of magnitude. While manual segmentation by clinicians takes around 20 min, our automatic segmentation takes less than a second with an average of 3 min manual correction time. Thus, our proposed solution can have a huge impact on the workload of clinical staff and on the standardization of care.