2026-04-17T20:13:37Zhttps://recercat.cat/oai/request

oai:recercat.cat:10256/248472024-05-22T09:51:13Zcom_2072_452955com_2072_2054col_2072_452957

00925njm 22002777a 4500 dc Abramova, Valeriia author Oliver i Malagelada, Arnau author Salvi, Joaquim author Terceño Izaga, Mikel author Silva Blas, Yolanda author Lladó Bardera, Xavier author 2024-03-23 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 Valeriia Abramova received an FPI grant from Ministerio de Ciencia, Innovación y Universidades with reference number PRE2021-099121. This work was supported under DPI2020-114769RB-I00 from Ministerio de Ciencia, Innovación y Universidades and also by the ICREA Academia program http://hdl.handle.net/10256/24847 Hemorràgia cerebral Brain -- Hemorrhage Tomografia Tomography Imatgeria mèdica Imaging systems in medicine An End-to-End Deep Learning Framework for Predicting Hematoma Expansion in Hemorrhagic Stroke Patients from CT Images