Clinical evaluation of a novel atlas-based PET/CT brain image segmentation and quantification method for epilepsy

Abstract

Background: Positron emission tomography (PET)/computed tomography (CT) with [ 18 F] fluorodeoxyglucose {[ 18 F]FDG} has been shown to be an effective imaging method for the lateralization and localization of epilepsy. However, the efficacy of PET/CT image processing and analysis needs to be improved for clinical application. Our previous research proposed a novel atlas-based image method for PET brain image segmentation and quantification; in this study, we evaluated its effectiveness in clinical patients.Methods: For image segmentation, a head anatomy template was registered to the subject image by integrating dual-modality image registration and landmark-constraint. The localizations of abnormalities were examined by quantitative comparison using the collected database. The PET/CT images of 20 reference patients and 11 patients with epilepsy were used to compare results between the proposed manual method and statistical parameter mapping (SPM). A dice coefficient analysis was performed on the six central brain regions to assess the segmentation effectiveness, and the diagnostic results of the epileptic regions were examined using pathological results as a reference. Results:The dice results of the proposed method were generally higher than those of SPM, with the averaged dice values for the proposed method and SPM being 0.78 and 0.55, respectively, in the reference group (P<0.001), and 0.73 and 0.48, respectively, in the epileptic group (P<0.001). Our proposed method detected all the pathologically reported epileptic defects; however, using the visual assessment method, epileptic defects were missed in three patients. Both the proposed and visual assessment methods incorrectly identified non-epileptic areas as epileptic areas. Conclusions:The results provide strong evidence of the feasibility of using our proposed method for accurate brain region segmentation in the diagnosis of epilepsy. Our atlas-based approach has promise for clinical application in the image processing and diagnosis of patients with epilepsy.

Impact of deep learning‐based multiorgan segmentation methods on patient‐specific internal dosimetry in PET/CT imaging: A comparative study

“…Many studies have investigated semi‐ and fully‐automated PET image segmentation for various applications. Atlas‐based methods are straightforward and show effectiveness for PET image segmentation and are commercially available in some devices 10 . The limitation of atlas‐based approaches is segmentation accuracy which depends highly on the image registration procedure owing to organ morphology.…”

“…Atlas‐based methods are straightforward and show effectiveness for PET image segmentation and are commercially available in some devices. 10 The limitation of atlas‐based approaches is segmentation accuracy which depends highly on the image registration procedure owing to organ morphology. Also, image artifacts and the unpredictability of tumor shape decrease the performance of the atlas‐based models.…”