title: Comparative performance evaluation of automated segmentation methods of hippocampus from magnetic resonance images of temporal lobe epilepsy patients.
abstract: OBJECTIVE
  Segmentation of the hippocampus from magnetic resonance (MR) images is a key task in the evaluation of mesial temporal lobe epilepsy (mTLE) patients. Several automated algorithms have been proposed although manual segmentation remains the benchmark. Choosing a reliable algorithm is problematic since structural definition pertaining to multiple edges, missing and fuzzy boundaries, and shape changes varies among mTLE subjects. Lack of statistical references and guidance for quantifying the reliability and reproducibility of automated techniques has further detracted from automated approaches. The purpose of this study was to develop a systematic and statistical approach using a large dataset for the evaluation of automated methods and establish a method that would achieve results better approximating those attained by manual tracing in the epileptogenic hippocampus.
  
  
  METHODS
  A template database of 195 (81 males, 114 females; age range 32-67 yr, mean 49.16 yr) MR images of mTLE patients was used in this study. Hippocampal segmentation was accomplished manually and by two well-known tools (FreeSurfer and hammer) and two previously published methods developed at their institution [Automatic brain structure segmentation (ABSS) and LocalInfo]. To establish which method was better performing for mTLE cases, several voxel-based, distance-based, and volume-based performance metrics were considered. Statistical validations of the results using automated techniques were compared with the results of benchmark manual segmentation. Extracted metrics were analyzed to find the method that provided a more similar result relative to the benchmark.
  
  
  RESULTS
  Among the four automated methods, ABSS generated the most accurate results. For this method, the Dice coefficient was 5.13%, 14.10%, and 16.67% higher, Hausdorff was 22.65%, 86.73%, and 69.58% lower, precision was 4.94%, -4.94%, and 12.35% higher, and the root mean square (RMS) was 19.05%, 61.90%, and 65.08% lower than LocalInfo, FreeSurfer, and hammer, respectively. The Bland-Altman similarity analysis revealed a low bias for the ABSS and LocalInfo techniques compared to the others.
  
  
  CONCLUSIONS
  The ABSS method for automated hippocampal segmentation outperformed other methods, best approximating what could be achieved by manual tracing. This study also shows that four categories of input data can cause automated segmentation methods to fail. They include incomplete studies, artifact, low signal-to-noise ratio, and inhomogeneity. Different scanner platforms and pulse sequences were considered as means by which to improve reliability of the automated methods. Other modifications were specially devised to enhance a particular method assessed in this study.
authors.affiliation: Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, New Jersey 08854 and Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, Michigan 48202.
authors.forename: Mohammad-Parsa
authors.initials: MP
authors.lastname: Hosseini
authors.affiliation: Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, Michigan 48202.
authors.forename: Mohammad-Reza
authors.initials: MR
authors.lastname: Nazem-Zadeh
authors.affiliation: Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, New Jersey 08854.
authors.forename: Dario
authors.initials: D
authors.lastname: Pompili
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02129.
authors.forename: Kourosh
authors.initials: K
authors.lastname: Jafari-Khouzani
authors.affiliation: Department of Clinical Neuroscience, Spectrum Health System, Grand Rapids, Michigan 49503 and Division of Neurosurgery, College of Human Medicine, Michigan State University, Grand Rapids, Michigan 49503.
authors.forename: Kost
authors.initials: K
authors.lastname: Elisevich
authors.affiliation: Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, Michigan 48202; Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, University of Tehran, Tehran 1439957131, Iran; and School of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics (IPM), Tehran 1954856316, Iran.
authors.forename: Hamid
authors.initials: H
authors.lastname: Soltanian-Zadeh
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001331:Automation; D004833:Epilepsy, Temporal Lobe / Q000175:diagnosis*; D005260:Female; D006624:Hippocampus; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged