title: Patient-specific semi-supervised learning for postoperative brain tumor segmentation.
abstract: In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.
authors.forename: Raphael
authors.initials: R
authors.lastname: Meier
authors.forename: Stefan
authors.initials: S
authors.lastname: Bauer
authors.forename: Johannes
authors.initials: J
authors.lastname: Slotboom
authors.forename: Roland
authors.initials: R
authors.lastname: Wiest
authors.forename: Mauricio
authors.initials: M
authors.lastname: Reyes
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000150:complications / Q000473:pathology* / Q000601:surgery*; D003937:Diagnosis, Differential; D005910:Glioma / Q000150:complications / Q000473:pathology* / Q000601:surgery*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D019635:Neurosurgical Procedures / Q000009:adverse effects / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D011182:Postoperative Care / Q000379:methods; D019106:Postoperative Hemorrhage / Q000209:etiology / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016896:Treatment Outcome