| Autor: |
Casseb RF; Universidade Estadual de Campinas (UNICAMP), Neuroimaging Laboratory, Campinas, SP, Brazil., de Campos BM; Universidade Estadual de Campinas (UNICAMP), Neuroimaging Laboratory, Campinas, SP, Brazil., Loos WS; Advanced Imaging and Artificial Intelligence Lab, University of Calgary, Calgary, AB, Canada., Barbosa MER; School of Medical Sciences, Pontifical Catholic University of Campinas, Campinas, SP, Brazil., Alvim MKM; Universidade Estadual de Campinas (UNICAMP), Neuroimaging Laboratory, Campinas, SP, Brazil., Paulino GCL; Universidade Estadual de Campinas (UNICAMP), Neuroimaging Laboratory, Campinas, SP, Brazil., Pucci F; Cleveland Clinic Foundation, Cleveland, OH, United States of America., Worrell S; Cleveland Clinic Foundation, Cleveland, OH, United States of America., de Souza RM; Universidade Estadual de Campinas (UNICAMP), Neuroimaging Laboratory, Campinas, SP, Brazil., Jehi L; Cleveland Clinic Foundation, Cleveland, OH, United States of America., Cendes F; Universidade Estadual de Campinas (UNICAMP), Neuroimaging Laboratory, Campinas, SP, Brazil. |
| Abstrakt: |
The rapid and constant development of deep learning (DL) strategies is pushing forward the quality of object segmentation in images from diverse fields of interest. In particular, these algorithms can be very helpful in delineating brain abnormalities (lesions, tumors, lacunas, etc), enabling the extraction of information such as volume and location, that can inform doctors or feed predictive models. Here, we describe ResectVol DL, a fully automatic tool developed to segment resective lacunas in brain images of patients with epilepsy. ResectVol DL relies on the nnU-Net framework that leverages the 3D U-Net deep learning architecture. T1-weighted MRI datasets from 120 patients (57 women; 31.5 ± 15.9 years old at surgery) were used to train (n=78) and test (n=48) our tool. Manual segmentations were carried out by five different raters and were considered as ground truth for performance assessment. We compared ResectVol DL with two other fully automatic methods: ResectVol 1.1.2 and DeepResection, using the Dice similarity coefficient (DSC), Pearson's correlation coefficient, and relative difference to manual segmentation. ResectVol DL presented the highest median DSC (0.92 vs. 0.78 and 0.90), the highest correlation coefficient (0.99 vs. 0.63 and 0.94), and the lowest median relative difference (9 vs. 44 and 12 %). Overall, we demonstrate that ResectVol DL accurately segments brain lacunas, which has the potential to assist in the development of predictive models for postoperative cognitive and seizure outcomes. |
