Deep learning-based brain tumor bed segmentation for dynamic magnetic resonance perfusion imaging

Detecting and segmenting neoplasms are an important part of radiotherapy treatment planning, disease monitoring, and predicting patient outcome. In the brain, functional magnetic resonance imaging (MRI) like dynamic susceptibility contrast enhanced (DSC) MRI are important tools for disease monitoring post-surgical resection (post-op) and post-radiotherapy. However, the manual contouring of these neoplasms of the brain is time consuming, expensive, and introduces inter-observer variability. In this work, we propose to uDetecting and segmenting neoplasms are an important part of radiotherapy treatment planning, disease monitoring, and predicting patient outcome. In the brain, functional magnetic resonance imaging (MRI) like dynamic susceptibility contrast enhanced (DSC) MRI are important tools for disease monitoring post-surgical resection (post-op) and post-radiotherapy. However, the manual contouring of these neoplasms of the brain is time consuming, expensive, and introduces inter-observer variability. In this work, we propose to use a 3D Mask R-CNN method to automatically detect and segment the brain tumor bed contour for DSC MRI perfusion images. Sixteen patients’ perfusion sequence images, each with 60 time point volumes, were used in this study. Experimental results show that our proposed 3D Mask R-CNN method achieves an overall Dice similarity, precision, recall and volume of difference (VoD) were 82%±6%, 84%±3%, 89%±3% and 86%±3% respectively.