Unpaired MR-CT brain dataset for unsupervised image translation

Abstract

The data presented in this article deals with the problem of brain tumor image translation across different modalities. The provided dataset represents unpaired brain magnetic resonance (MR) and computed tomography (CT) image data volumes of 20 patients. This includes 179 two-dimensional (2D) axial MR and CT images. The MR cases are acquired using Siemens Verio scanner, while the CT images with a Siemens Somatom scanner. The MR and CT tumor volumes were collected, diagnosed and annotated by experienced radiologists specialized in oncology and radiotherapy. The collected image volumes can be useful for researchers working in the field of artificial intelligence (AI) applications for brain tumor detection, classification and segmentation in MR and CT modalities. The provided tumor masks per each tumor volume can assist data scientists with limited background in cancer imaging. Moreover, clinical interpretation is given per each tumor volume, which can assist in deep learning model training with multiple source data (non-imaging or textual data) as well. The provided dataset can facilitate for annotation-efficient lesion segmentation using bidirectional MR-CT cross-modality image translation.

Keywords

• MR and CT image dataset
• Image translation
• Unsupervised learning
• Brain tumors