Improving Pathological Structure Segmentation via Transfer Learning Across Diseases

One of the biggest challenges in developing robust machine learning techniques for medical image analysis is the lack of access to large-scale annotated image datasets needed for supervised learning. When the task is to segment pathological structures (e.g. lesions, tumors) from patient images, training on a dataset with few samples is very challenging due to the large class imbalance and inter-subject variability. In this paper, we explore how to best leverage a segmentation model that has been pre-trained on a large dataset of patients images with one disease in order to successfully train a deep learning pathology segmentation model for a different disease, for which only a relatively small patient dataset is available. Specifically, we train a UNet model on a large-scale, proprietary, multi-center, multi-scanner Multiple Sclerosis (MS) clinical trial dataset containing over 3500 multi-modal MRI samples with expert-derived lesion labels. We explore several transfer learning approaches to leverage the learned MS model for the task of multi-class brain tumor segmentation on the BraTS 2018 dataset. Our results indicate that adapting and fine-tuning the encoder and decoder of the network trained on the larger MS dataset leads to improvement in brain tumor segmentation when few instances are available. This type of transfer learning outperforms training and testing the network on the BraTS dataset from scratch as well as several other transfer learning approaches, particularly when only a small subset of the dataset is available.