Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI.

ABSTRACT: Accurate segmentation of surgical resection sites is critical for clinical assessments and neuroimaging research applications, including resection extent determination, predictive modeling of surgery outcome, and masking image processing near resection sites. In this study, an automated resection cavity segmentation algorithm is developed for analyzing postoperative MRI of epilepsy patients and deployed in an easy-to-use graphical user interface (GUI) that estimates remnant brain volumes, including postsurgical hippocampal remnant tissue. This retrospective study included postoperative T1-weighted MRI from 62 temporal lobe epilepsy (TLE) patients who underwent resective surgery. The resection site was manually segmented and reviewed by a neuroradiologist (JMS). A majority vote ensemble alg

SUBMITTER: Arnold TC 

PROVIDER: S-EPMC9402390 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature