An End-to-End Deep Learning Framework for Predicting Hematoma Expansion in Hemorrhagic Stroke Patients from CT Images

Abstract

Hematoma expansion (HE) occurs in 20% of patients with hemorrhagic stroke within 24 h of onset, and it is associated with a poorer patient outcome. From a clinical point of view, predicting HE from the initial patient computed tomography (CT) image is useful to improve therapeutic decisions and minimize prognosis errors. In this work, we propose an end-to-end deep learning framework for predicting the final hematoma expansion and its corresponding lesion mask. We also explore the problem of having limited data and propose to augment the available dataset with synthetic images. The obtained results show an improved HE prediction when incorporating the use of synthetic images into the model, with a mean Dice score of the HE growth area of 0.506 and an average prediction error in hematoma volume of −3.44 mL. The proposed approach achieved results in line with state-of-the-art methods with far fewer data by using synthetic image generation and without requiring the inclusion of patient clinical data.

Keywords

• hemorrhagic stroke
• hematoma expansion
• deep learning
• hemorrhage growth prediction