Fully convolutional neural network-based segmentation of brain metastases: a comprehensive approach for accurate detection and localization

Abstract

Brain metastases present a formidable challenge in cancer management due to the infiltration of malignant cells from distant sites into the brain. Precise segmentation of brain metastases (BM) in medical imaging is vital for treatment planning and assessment. Leveraging deep learning techniques has shown promise in automating BM identification, facilitating faster and more accurate detection. This paper aims to develop an innovative novel deep learning model tailored for BM segmentation, addressing current approach limitations. Utilizing a comprehensive dataset of annotated magnetic resonance imaging (MRI) from Stanford University, the proposed model will undergo thorough evaluation using standard performance metrics. Comparative analysis with existing segmentation methods will highlight the superior performance and efficacy of our model. The anticipated outcome of this research is a highly accurate and efficient deep learning model for brain metastasis segmentation. Such a model holds potential to enhance treatment planning, monitoring, and ultimately improve patient care and clinical outcomes in managing brain metastases.