Feasibility Study of Detecting and Segmenting Small Brain Tumors in a Small MRI Dataset with Self-Supervised Learning

Abstract

Objectives: This paper studies the segmentation and detection of small metastatic brain tumors. This study aims to evaluate the feasibility of training a deep neural network for the segmentation and detection of metastatic brain tumors in MRI using a very small dataset of 33 cases, by leveraging large public datasets of primary tumors; Methods: This study explores various methods, including supervised learning, two transfer learning approaches, and self-supervised learning, utilizing U-net and Swin UNETR models; Results: The self-supervised learning approach utilizing the Swin UNETR model yielded the best performance. The Dice score for small brain tumors was approximately 0.19. Sensitivity reached 100%, while specificity was 54.5%. When excluding subjects with hyperintensities, the specificity improved to 80.0%; Conclusions: It is feasible to train a model using self-supervised learning and a small dataset for the segmentation and detection of small brain tumors.