Automatic Postoperative Brain Tumor Segmentation with Limited Data using Transfer Learning and Triplet Attention

Abstract

Accurate brain tumor segmentation is clinically important for diagnosis and treatment planning. Convolutional neural networks (CNNs) have achieved promising performance in various visual recognition tasks. Training such networks usually requires large amount of labeled data, which is often challenging for medical applications. In this work, we address the segmentation problem by applying transfer learning to downstream segmentation tasks. Specifically, we explore how knowledge acquired from a large preoperative dataset can be transferred to postoperative tumor segmentation on a smaller dataset. To this end, we have developed a 3D CNN for brain tumor segmentation, and fine-tuned the pretrained models on the target domain data. To better exploit the inter-channel and spatial information, triplet attention has been incorporated and extended into existing segmentation network. Extensive experiments on our dataset demonstrate the effectiveness of transfer learning and attention modules for improved postoperative tumor segmentation performance when only limited amount of annotated data is available.